kittycad.models.modeling_cmd
Classes
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Camera drag ended |
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Camera drag continued. |
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Camera drag started. |
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Get the center of mass of entities in the scene or the default scene. |
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Closes a path, converting it to a 2D solid. |
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Get control points of the given curve. |
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Find the start and end of a curve. |
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Get type of the given curve. |
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Get the plane for sketch mode. |
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Updates the camera to center to the center of the current scene's bounds |
Updates the camera to center to the center of the current selection (or the origin if nothing is selected) |
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Focus the default camera upon an object in the scene. |
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Gets the default camera's camera settings |
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Change what the default camera is looking at. |
Change what the default camera is looking at. |
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Use orthographic projection. |
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Use perspective projection. |
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Adjust zoom of the default camera. |
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Get the density of entities in the scene or the default scene. |
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Sets whether or not changes to the scene or its objects will be done as a "dry run" In a dry run, successful commands won't actually change the model. |
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Changes visibility of scene-wide edge lines on brep solids |
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Sets whether or not changes to the scene or its objects will be done as a "dry run" In a dry run, successful commands won't actually change the model. |
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Sketch on some entity (e.g. a plane, a face). |
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Evaluates the position of a path in one shot (engine utility for kcl executor) |
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Create a circular pattern using this entity. |
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Fade entity in or out. |
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What are all UUIDs of this entity's children? |
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What is the UUID of this entity's n-th child? |
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What is the distance between these two entities? |
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How many children does the entity have? |
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What is this entity's parent? |
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What are all UUIDs of all the paths sketched on top of this entity? |
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Create a linear pattern using this entity. |
Create a pattern using this entity by specifying the transform for each desired repetition. |
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Create a helix using the input cylinder and other specified parameters. |
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Mirror the input entities over the specified axis. |
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Mirror the input entities over the specified edge. |
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Set opacity of the entity. |
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Export the scene to a file. |
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Extend a path by adding a new segment which starts at the path's "pen". |
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Command for extruding a solid 2d. |
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Obtains the surface "center of mass" |
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Determines the gradient (dFdu, dFdv) + normal vector on a brep face evaluated by parameters u,v |
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Determines a position on a brep face evaluated by parameters u,v |
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Determines whether a brep face is planar and returns its surface-local planar axes if so |
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What type of entity is this? |
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Get the number of objects in the scene |
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Get the plane for sketch mode. |
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Stop dragging the mouse. |
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Continue dragging the mouse. |
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Start dragging the mouse. |
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Changes the current highlighted entity to these entities. |
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Changes the current highlighted entity to whichever one is at the given window coordinate. |
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Import files to the current model. |
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Command for lofting sections to create a solid |
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Add a gizmo showing the axes. |
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Make a new path by offsetting an object by a given distance. |
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Make a new plane |
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Get the mass of entities in the scene or the default scene. |
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Send a mouse click event Updates modified/selected entities. |
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Send a mouse move event |
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Move the path's "pen". |
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Create a new annotation |
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Bring an object to the front of the scene |
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Set the material properties of an object |
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Hide or show an object |
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Obtain curve id by index |
Obtain curve ids for vertex ids |
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Query the given path. |
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Obtain the sketch target id (if the path was drawn in sketchmode) for a path |
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Obtain vertex ids for a path |
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Utility method. |
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Set the color of a plane. |
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Reconfigure the stream. |
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Remove scene objects. |
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Command for revolving a solid 2d. |
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Command for revolving a solid 2d about a brep edge |
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Removes all of the Objects in the scene |
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Adds one or more entities (by UUID) to the selection. |
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Clear the selection |
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Find all IDs of selected entities |
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Removes one or more entities (by UUID) from the selection. |
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Replaces current selection with these entities (by UUID). |
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Modifies the selection by simulating a "mouse click" at the given x,y window coordinate Returns ID of whatever was selected. |
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Send object to front or back. |
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Set the background color of the scene. |
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Set the properties of the tool lines for the scene. |
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Set the default system properties used when a specific property isn't set. |
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Set the units of the scene. |
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What kind of entities can be selected? |
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When you select some entity with the current tool, what should happen to the entity? |
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Set the current tool. |
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Disable sketch mode. |
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Add a hole to a Solid2d object before extruding it. |
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Fillets the given edge with the specified radius. |
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Gets all faces which use the given edge. |
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Gets all edges which are opposite the given edge, across all possible faces. |
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Gets the shared edge between these two faces if it exists |
Get a concise description of all of an extrusion's faces. |
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Gets the next adjacent edge for the given edge, along the given face. |
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Gets the edge opposite the given edge, along the given face. |
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Gets the previous adjacent edge for the given edge, along the given face. |
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Command for shelling a solid3d face |
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Start a new path. |
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Get the surface area of entities in the scene or the default scene. |
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Take a snapshot of the current view. |
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Update an annotation |
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Fit the view to the scene with an isometric view. |
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Get the volume of entities in the scene or the default scene. |
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Fit the view to the specified object(s). |
- class kittycad.models.modeling_cmd.OptionCameraDragEnd(**data)[source][source]
Camera drag ended
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'interaction': <enum 'CameraDragInteractionType'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['camera_drag_end'], 'window': <class 'kittycad.models.point2d.Point2d'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionCameraDragEnd'>, 'config': {'title': 'OptionCameraDragEnd'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionCameraDragEnd'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionCameraDragEnd'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionCameraDragEnd:94467866658000', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'interaction': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'CameraDragInteractionType'>, 'members': [CameraDragInteractionType.PAN, CameraDragInteractionType.ROTATE, CameraDragInteractionType.ZOOM], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.camera_drag_interaction_type.CameraDragInteractionType:94467849400224', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'camera_drag_end', 'schema': {'expected': ['camera_drag_end'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'window': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'OptionCameraDragEnd', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a284d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc6e130, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "camera_drag_end", }, expected_py: None, name: "literal['camera_drag_end']", }, ), }, ), ), required: true, }, "interaction": SerField { key_py: Py( 0x00007fa14e7e0670, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb029b2fa0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "window": SerField { key_py: Py( 0x00007fa14e1645a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionCameraDragEnd", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCameraDragEnd", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "interaction", lookup_key: Simple { key: "interaction", py_key: Py( 0x00007fa14ae89ab0, ), path: LookupPath( [ S( "interaction", Py( 0x00007fa14ae89a70, ), ), ], ), }, name_py: Py( 0x00007fa14e7e0670, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb029b2fa0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "rotate": 1, "zoom": 2, "pan": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b38d970, ), Py( 0x00007fa14b38d9d0, ), Py( 0x00007fa14b38da30, ), ], }, missing: None, expected_repr: "'pan', 'rotate' or 'zoom'", strict: false, class_repr: "CameraDragInteractionType", name: "str-enum[CameraDragInteractionType]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b0201e0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b020210, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc6e130, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "camera_drag_end": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc6e130, ), ], }, expected_repr: "'camera_drag_end'", name: "literal['camera_drag_end']", }, ), validate_default: false, copy_default: false, name: "default[literal['camera_drag_end']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "window", lookup_key: Simple { key: "window", py_key: Py( 0x00007fa14b020240, ), path: LookupPath( [ S( "window", Py( 0x00007fa14b020270, ), ), ], ), }, name_py: Py( 0x00007fa14e1645a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae89940, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae89940, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), frozen: false, }, ], model_name: "OptionCameraDragEnd", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a284d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCameraDragEnd", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, interaction: kittycad.models.camera_drag_interaction_type.CameraDragInteractionType, type: Literal['camera_drag_end'] = 'camera_drag_end', window: kittycad.models.point2d.Point2d) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
-
interaction:
CameraDragInteractionType[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'interaction': FieldInfo(annotation=CameraDragInteractionType, required=True), 'type': FieldInfo(annotation=Literal['camera_drag_end'], required=False, default='camera_drag_end'), 'window': FieldInfo(annotation=Point2d, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionCameraDragMove(**data)[source][source]
Camera drag continued.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'interaction': <enum 'CameraDragInteractionType'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'sequence': typing.Optional[int], 'type': typing.Literal['camera_drag_move'], 'window': <class 'kittycad.models.point2d.Point2d'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionCameraDragMove'>, 'config': {'title': 'OptionCameraDragMove'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionCameraDragMove'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionCameraDragMove'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionCameraDragMove:94467866642944', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'interaction': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'CameraDragInteractionType'>, 'members': [CameraDragInteractionType.PAN, CameraDragInteractionType.ROTATE, CameraDragInteractionType.ZOOM], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.camera_drag_interaction_type.CameraDragInteractionType:94467849400224', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'sequence': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'camera_drag_move', 'schema': {'expected': ['camera_drag_move'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'window': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'OptionCameraDragMove', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a24a00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc6deb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "camera_drag_move", }, expected_py: None, name: "literal['camera_drag_move']", }, ), }, ), ), required: true, }, "sequence": SerField { key_py: Py( 0x00007fa14fa3b790, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Int( IntSerializer, ), }, ), }, ), ), required: true, }, "window": SerField { key_py: Py( 0x00007fa14e1645a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), ), required: true, }, "interaction": SerField { key_py: Py( 0x00007fa14e7e0670, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb029b2fa0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "OptionCameraDragMove", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCameraDragMove", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "interaction", lookup_key: Simple { key: "interaction", py_key: Py( 0x00007fa14ae880b0, ), path: LookupPath( [ S( "interaction", Py( 0x00007fa14ae88070, ), ), ], ), }, name_py: Py( 0x00007fa14e7e0670, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb029b2fa0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "zoom": 2, "pan": 0, "rotate": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b38d970, ), Py( 0x00007fa14b38d9d0, ), Py( 0x00007fa14b38da30, ), ], }, missing: None, expected_repr: "'pan', 'rotate' or 'zoom'", strict: false, class_repr: "CameraDragInteractionType", name: "str-enum[CameraDragInteractionType]", }, ), frozen: false, }, Field { name: "sequence", lookup_key: Simple { key: "sequence", py_key: Py( 0x00007fa14ae88130, ), path: LookupPath( [ S( "sequence", Py( 0x00007fa14ae880f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3b790, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Int( IntValidator { strict: false, }, ), name: "nullable[int]", }, ), validate_default: false, copy_default: false, name: "default[nullable[int]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe7b40, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe7b70, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc6deb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "camera_drag_move": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc6deb0, ), ], }, expected_repr: "'camera_drag_move'", name: "literal['camera_drag_move']", }, ), validate_default: false, copy_default: false, name: "default[literal['camera_drag_move']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "window", lookup_key: Simple { key: "window", py_key: Py( 0x00007fa14afe7ba0, ), path: LookupPath( [ S( "window", Py( 0x00007fa14afe7bd0, ), ), ], ), }, name_py: Py( 0x00007fa14e1645a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae37f00, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae37f00, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), frozen: false, }, ], model_name: "OptionCameraDragMove", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a24a00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCameraDragMove", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, interaction: kittycad.models.camera_drag_interaction_type.CameraDragInteractionType, sequence: Optional[int] = None, type: Literal['camera_drag_move'] = 'camera_drag_move', window: kittycad.models.point2d.Point2d) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
-
interaction:
CameraDragInteractionType[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'interaction': FieldInfo(annotation=CameraDragInteractionType, required=True), 'sequence': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['camera_drag_move'], required=False, default='camera_drag_move'), 'window': FieldInfo(annotation=Point2d, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionCameraDragStart(**data)[source][source]
Camera drag started.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'interaction': <enum 'CameraDragInteractionType'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['camera_drag_start'], 'window': <class 'kittycad.models.point2d.Point2d'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionCameraDragStart'>, 'config': {'title': 'OptionCameraDragStart'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionCameraDragStart'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionCameraDragStart'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionCameraDragStart:94467866634688', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'interaction': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'CameraDragInteractionType'>, 'members': [CameraDragInteractionType.PAN, CameraDragInteractionType.ROTATE, CameraDragInteractionType.ZOOM], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.camera_drag_interaction_type.CameraDragInteractionType:94467849400224', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'camera_drag_start', 'schema': {'expected': ['camera_drag_start'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'window': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'OptionCameraDragStart', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a229c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "interaction": SerField { key_py: Py( 0x00007fa14e7e0670, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb029b2fa0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "window": SerField { key_py: Py( 0x00007fa14e1645a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc6ff70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "camera_drag_start", }, expected_py: None, name: "literal['camera_drag_start']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionCameraDragStart", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCameraDragStart", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "interaction", lookup_key: Simple { key: "interaction", py_key: Py( 0x00007fa14ae36270, ), path: LookupPath( [ S( "interaction", Py( 0x00007fa14ae36230, ), ), ], ), }, name_py: Py( 0x00007fa14e7e0670, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb029b2fa0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "pan": 0, "rotate": 1, "zoom": 2, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b38d970, ), Py( 0x00007fa14b38d9d0, ), Py( 0x00007fa14b38da30, ), ], }, missing: None, expected_repr: "'pan', 'rotate' or 'zoom'", strict: false, class_repr: "CameraDragInteractionType", name: "str-enum[CameraDragInteractionType]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe7990, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe7450, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc6ff70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "camera_drag_start": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc6ff70, ), ], }, expected_repr: "'camera_drag_start'", name: "literal['camera_drag_start']", }, ), validate_default: false, copy_default: false, name: "default[literal['camera_drag_start']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "window", lookup_key: Simple { key: "window", py_key: Py( 0x00007fa14afe74e0, ), path: LookupPath( [ S( "window", Py( 0x00007fa14afe7390, ), ), ], ), }, name_py: Py( 0x00007fa14e1645a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae360c0, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae360c0, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), frozen: false, }, ], model_name: "OptionCameraDragStart", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a229c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCameraDragStart", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, interaction: kittycad.models.camera_drag_interaction_type.CameraDragInteractionType, type: Literal['camera_drag_start'] = 'camera_drag_start', window: kittycad.models.point2d.Point2d) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
-
interaction:
CameraDragInteractionType[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'interaction': FieldInfo(annotation=CameraDragInteractionType, required=True), 'type': FieldInfo(annotation=Literal['camera_drag_start'], required=False, default='camera_drag_start'), 'window': FieldInfo(annotation=Point2d, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionCenterOfMass(**data)[source][source]
Get the center of mass of entities in the scene or the default scene.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entity_ids': typing.List[str], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'output_unit': <enum 'UnitLength'>, 'type': typing.Literal['center_of_mass']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionCenterOfMass'>, 'config': {'title': 'OptionCenterOfMass'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionCenterOfMass'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionCenterOfMass'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionCenterOfMass:94467868073968', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitLength'>, 'members': [UnitLength.CM, UnitLength.FT, UnitLength.IN, UnitLength.M, UnitLength.MM, UnitLength.YD], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_length.UnitLength:94467861018320', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'center_of_mass', 'schema': {'expected': ['center_of_mass'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCenterOfMass', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b81ff0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc6fb30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "center_of_mass", }, expected_py: None, name: "literal['center_of_mass']", }, ), }, ), ), required: true, }, "entity_ids": SerField { key_py: Py( 0x00007fa14b038970, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "output_unit": SerField { key_py: Py( 0x00007fa14bb4f470, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb034c76d0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionCenterOfMass", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCenterOfMass", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007fa14adeaf70, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007fa14adeaf30, ), ), ], ), }, name_py: Py( 0x00007fa14b038970, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007fa14adeaff0, ), path: LookupPath( [ S( "output_unit", Py( 0x00007fa14adeafb0, ), ), ], ), }, name_py: Py( 0x00007fa14bb4f470, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb034c76d0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "ft": 1, "m": 3, "yd": 5, "mm": 4, "cm": 0, "in": 2, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c4c50, ), Py( 0x00007fa14b3c51f0, ), Py( 0x00007fa14b3c52b0, ), Py( 0x00007fa14b3c5310, ), Py( 0x00007fa14b3c5370, ), Py( 0x00007fa14b3c53d0, ), ], }, missing: None, expected_repr: "'cm', 'ft', 'in', 'm', 'mm' or 'yd'", strict: false, class_repr: "UnitLength", name: "str-enum[UnitLength]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc71b0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc71e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc6fb30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "center_of_mass": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc6fb30, ), ], }, expected_repr: "'center_of_mass'", name: "literal['center_of_mass']", }, ), validate_default: false, copy_default: false, name: "default[literal['center_of_mass']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionCenterOfMass", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b81ff0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCenterOfMass", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entity_ids: List[str], output_unit: kittycad.models.unit_length.UnitLength, type: Literal['center_of_mass'] = 'center_of_mass') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entity_ids': FieldInfo(annotation=List[str], required=True), 'output_unit': FieldInfo(annotation=UnitLength, required=True), 'type': FieldInfo(annotation=Literal['center_of_mass'], required=False, default='center_of_mass')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
-
output_unit:
UnitLength[source]
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionClosePath(**data)[source][source]
Closes a path, converting it to a 2D solid.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'path_id': <class 'str'>, 'type': typing.Literal['close_path']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionClosePath'>, 'config': {'title': 'OptionClosePath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionClosePath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionClosePath'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionClosePath:94467866618672', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'path_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'close_path', 'schema': {'expected': ['close_path'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionClosePath', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a1eb30, ), serializer: Fields( GeneralFieldsSerializer { fields: { "path_id": SerField { key_py: Py( 0x00007fa14afd3120, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc6f5b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "close_path", }, expected_py: None, name: "literal['close_path']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionClosePath", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionClosePath", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "path_id", lookup_key: Simple { key: "path_id", py_key: Py( 0x00007fa14afe7570, ), path: LookupPath( [ S( "path_id", Py( 0x00007fa14afe75a0, ), ), ], ), }, name_py: Py( 0x00007fa14afd3120, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe75d0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe7600, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc6f5b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "close_path": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc6f5b0, ), ], }, expected_repr: "'close_path'", name: "literal['close_path']", }, ), validate_default: false, copy_default: false, name: "default[literal['close_path']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionClosePath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a1eb30, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionClosePath", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, path_id: str, type: Literal['close_path'] = 'close_path') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'path_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['close_path'], required=False, default='close_path')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionCurveGetControlPoints(**data)[source][source]
Get control points of the given curve.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'curve_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['curve_get_control_points']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionCurveGetControlPoints'>, 'config': {'title': 'OptionCurveGetControlPoints'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionCurveGetControlPoints'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionCurveGetControlPoints'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionCurveGetControlPoints:94467867792672', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'curve_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'curve_get_control_points', 'schema': {'expected': ['curve_get_control_points'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCurveGetControlPoints', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b3d520, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd06c90, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "curve_get_control_points", }, expected_py: None, name: "literal['curve_get_control_points']", }, ), }, ), ), required: true, }, "curve_id": SerField { key_py: Py( 0x00007fa14b09ad70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCurveGetControlPoints", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCurveGetControlPoints", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "curve_id", lookup_key: Simple { key: "curve_id", py_key: Py( 0x00007fa14ada84f0, ), path: LookupPath( [ S( "curve_id", Py( 0x00007fa14ada84b0, ), ), ], ), }, name_py: Py( 0x00007fa14b09ad70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad56790, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad567c0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd06c90, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "curve_get_control_points": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd06c90, ), ], }, expected_repr: "'curve_get_control_points'", name: "literal['curve_get_control_points']", }, ), validate_default: false, copy_default: false, name: "default[literal['curve_get_control_points']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionCurveGetControlPoints", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b3d520, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCurveGetControlPoints", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, curve_id: str, type: Literal['curve_get_control_points'] = 'curve_get_control_points') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'curve_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['curve_get_control_points'], required=False, default='curve_get_control_points')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionCurveGetEndPoints(**data)[source][source]
Find the start and end of a curve.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'curve_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['curve_get_end_points']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionCurveGetEndPoints'>, 'config': {'title': 'OptionCurveGetEndPoints'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionCurveGetEndPoints'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionCurveGetEndPoints'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionCurveGetEndPoints:94467867923232', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'curve_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'curve_get_end_points', 'schema': {'expected': ['curve_get_end_points'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCurveGetEndPoints', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b5d320, ), serializer: Fields( GeneralFieldsSerializer { fields: { "curve_id": SerField { key_py: Py( 0x00007fa14b09ad70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb7ef0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "curve_get_end_points", }, expected_py: None, name: "literal['curve_get_end_points']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCurveGetEndPoints", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCurveGetEndPoints", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "curve_id", lookup_key: Simple { key: "curve_id", py_key: Py( 0x00007fa14add1270, ), path: LookupPath( [ S( "curve_id", Py( 0x00007fa14add11f0, ), ), ], ), }, name_py: Py( 0x00007fa14b09ad70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc5080, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc50b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb7ef0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "curve_get_end_points": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb7ef0, ), ], }, expected_repr: "'curve_get_end_points'", name: "literal['curve_get_end_points']", }, ), validate_default: false, copy_default: false, name: "default[literal['curve_get_end_points']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionCurveGetEndPoints", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b5d320, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCurveGetEndPoints", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, curve_id: str, type: Literal['curve_get_end_points'] = 'curve_get_end_points') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'curve_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['curve_get_end_points'], required=False, default='curve_get_end_points')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionCurveGetType(**data)[source][source]
Get type of the given curve.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'curve_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['curve_get_type']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionCurveGetType'>, 'config': {'title': 'OptionCurveGetType'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionCurveGetType'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionCurveGetType'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionCurveGetType:94467867776464', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'curve_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'curve_get_type', 'schema': {'expected': ['curve_get_type'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCurveGetType', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b395d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb7f70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "curve_get_type", }, expected_py: None, name: "literal['curve_get_type']", }, ), }, ), ), required: true, }, "curve_id": SerField { key_py: Py( 0x00007fa14b09ad70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCurveGetType", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCurveGetType", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "curve_id", lookup_key: Simple { key: "curve_id", py_key: Py( 0x00007fa14ad9f330, ), path: LookupPath( [ S( "curve_id", Py( 0x00007fa14ad9f3b0, ), ), ], ), }, name_py: Py( 0x00007fa14b09ad70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad56220, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad56250, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb7f70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "curve_get_type": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb7f70, ), ], }, expected_repr: "'curve_get_type'", name: "literal['curve_get_type']", }, ), validate_default: false, copy_default: false, name: "default[literal['curve_get_type']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionCurveGetType", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b395d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCurveGetType", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, curve_id: str, type: Literal['curve_get_type'] = 'curve_get_type') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'curve_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['curve_get_type'], required=False, default='curve_get_type')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionCurveSetConstraint(**data)[source][source]
Get the plane for sketch mode.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'constraint_bound': <enum 'PathComponentConstraintBound'>, 'constraint_type': <enum 'PathComponentConstraintType'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['curve_set_constraint']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionCurveSetConstraint'>, 'config': {'title': 'OptionCurveSetConstraint'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionCurveSetConstraint'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionCurveSetConstraint'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionCurveSetConstraint:94467867688720', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'constraint_bound': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'PathComponentConstraintBound'>, 'members': [PathComponentConstraintBound.UNCONSTRAINED, PathComponentConstraintBound.PARTIALLY_CONSTRAINED, PathComponentConstraintBound.FULLY_CONSTRAINED], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.path_component_constraint_bound.PathComponentConstraintBound:94467866073616', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'constraint_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'PathComponentConstraintType'>, 'members': [PathComponentConstraintType.UNCONSTRAINED, PathComponentConstraintType.VERTICAL, PathComponentConstraintType.HORIZONTAL, PathComponentConstraintType.EQUAL_LENGTH, PathComponentConstraintType.PARALLEL, PathComponentConstraintType.ANGLE_BETWEEN], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.path_component_constraint_type.PathComponentConstraintType:94467866076960', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'curve_set_constraint', 'schema': {'expected': ['curve_set_constraint'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCurveSetConstraint', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b23f10, ), serializer: Fields( GeneralFieldsSerializer { fields: { "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "constraint_type": SerField { key_py: Py( 0x00007fa14b01e9b0, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb0399a720, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb7e30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "curve_set_constraint", }, expected_py: None, name: "literal['curve_set_constraint']", }, ), }, ), ), required: true, }, "constraint_bound": SerField { key_py: Py( 0x00007fa14b01e930, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb03999a10, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "OptionCurveSetConstraint", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCurveSetConstraint", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "constraint_bound", lookup_key: Simple { key: "constraint_bound", py_key: Py( 0x00007fa14aef20b0, ), path: LookupPath( [ S( "constraint_bound", Py( 0x00007fa14ad2deb0, ), ), ], ), }, name_py: Py( 0x00007fa14b01e930, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb03999a10, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "fully_constrained": 2, "partially_constrained": 1, "unconstrained": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b11ae70, ), Py( 0x00007fa14b11af30, ), Py( 0x00007fa14b11aff0, ), ], }, missing: None, expected_repr: "'unconstrained', 'partially_constrained' or 'fully_constrained'", strict: false, class_repr: "PathComponentConstraintBound", name: "str-enum[PathComponentConstraintBound]", }, ), frozen: false, }, Field { name: "constraint_type", lookup_key: Simple { key: "constraint_type", py_key: Py( 0x00007fa14ad6c4b0, ), path: LookupPath( [ S( "constraint_type", Py( 0x00007fa14ad6c530, ), ), ], ), }, name_py: Py( 0x00007fa14b01e9b0, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb0399a720, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "unconstrained": 0, "angle_between": 5, "equal_length": 3, "vertical": 1, "horizontal": 2, "parallel": 4, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b11b350, ), Py( 0x00007fa14b11b2f0, ), Py( 0x00007fa14b11bc50, ), Py( 0x00007fa14b11bcb0, ), Py( 0x00007fa14b11bd10, ), Py( 0x00007fa14b11bd70, ), ], }, missing: None, expected_repr: "'unconstrained', 'vertical', 'horizontal', 'equal_length', 'parallel' or 'angle_between'", strict: false, class_repr: "PathComponentConstraintType", name: "str-enum[PathComponentConstraintType]", }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14ad6c3f0, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14ad6c430, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b024300, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b024360, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb7e30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "curve_set_constraint": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb7e30, ), ], }, expected_repr: "'curve_set_constraint'", name: "literal['curve_set_constraint']", }, ), validate_default: false, copy_default: false, name: "default[literal['curve_set_constraint']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionCurveSetConstraint", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b23f10, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCurveSetConstraint", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, constraint_bound: kittycad.models.path_component_constraint_bound.PathComponentConstraintBound, constraint_type: kittycad.models.path_component_constraint_type.PathComponentConstraintType, object_id: str, type: Literal['curve_set_constraint'] = 'curve_set_constraint') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
-
constraint_bound:
PathComponentConstraintBound[source]
-
constraint_type:
PathComponentConstraintType[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'constraint_bound': FieldInfo(annotation=PathComponentConstraintBound, required=True), 'constraint_type': FieldInfo(annotation=PathComponentConstraintType, required=True), 'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['curve_set_constraint'], required=False, default='curve_set_constraint')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionDefaultCameraCenterToScene(**data)[source][source]
Updates the camera to center to the center of the current scene’s bounds
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'camera_movement': <enum 'CameraMovement'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_center_to_scene']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionDefaultCameraCenterToScene'>, 'config': {'title': 'OptionDefaultCameraCenterToScene'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionDefaultCameraCenterToScene'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionDefaultCameraCenterToScene'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionDefaultCameraCenterToScene:94467868189760', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'camera_movement': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'vantage', 'schema': {'cls': <enum 'CameraMovement'>, 'members': [CameraMovement.VANTAGE, CameraMovement.NONE], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.camera_movement.CameraMovement:94467849439920', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_center_to_scene', 'schema': {'expected': ['default_camera_center_to_scene'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraCenterToScene', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b9e440, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd07000, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_center_to_scene", }, expected_py: None, name: "literal['default_camera_center_to_scene']", }, ), }, ), ), required: true, }, "camera_movement": SerField { key_py: Py( 0x00007fa14bc6fef0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bbeb7b0, ), ), serializer: Enum( EnumSerializer { class: Py( 0x000055eb029bcab0, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraCenterToScene", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraCenterToScene", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "camera_movement", lookup_key: Simple { key: "camera_movement", py_key: Py( 0x00007fa14ae071f0, ), path: LookupPath( [ S( "camera_movement", Py( 0x00007fa14ae070b0, ), ), ], ), }, name_py: Py( 0x00007fa14bc6fef0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bbeb7b0, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb029bcab0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "vantage": 0, "none": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b38dcd0, ), Py( 0x00007fa14b38dd30, ), ], }, missing: None, expected_repr: "'vantage' or 'none'", strict: false, class_repr: "CameraMovement", name: "str-enum[CameraMovement]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[CameraMovement]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae005a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae004e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd07000, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_center_to_scene": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd07000, ), ], }, expected_repr: "'default_camera_center_to_scene'", name: "literal['default_camera_center_to_scene']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_center_to_scene']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraCenterToScene", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b9e440, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraCenterToScene", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, camera_movement: kittycad.models.camera_movement.CameraMovement = 'vantage', type: Literal['default_camera_center_to_scene'] = 'default_camera_center_to_scene') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
-
camera_movement:
CameraMovement[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'camera_movement': FieldInfo(annotation=CameraMovement, required=False, default='vantage'), 'type': FieldInfo(annotation=Literal['default_camera_center_to_scene'], required=False, default='default_camera_center_to_scene')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionDefaultCameraCenterToSelection(**data)[source][source]
Updates the camera to center to the center of the current selection (or the origin if nothing is selected)
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'camera_movement': <enum 'CameraMovement'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_center_to_selection']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionDefaultCameraCenterToSelection'>, 'config': {'title': 'OptionDefaultCameraCenterToSelection'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionDefaultCameraCenterToSelection'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionDefaultCameraCenterToSelection'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionDefaultCameraCenterToSelection:94467868169696', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'camera_movement': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'vantage', 'schema': {'cls': <enum 'CameraMovement'>, 'members': [CameraMovement.VANTAGE, CameraMovement.NONE], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.camera_movement.CameraMovement:94467849439920', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_center_to_selection', 'schema': {'expected': ['default_camera_center_to_selection'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraCenterToSelection', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b995e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2ad30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_center_to_selection", }, expected_py: None, name: "literal['default_camera_center_to_selection']", }, ), }, ), ), required: true, }, "camera_movement": SerField { key_py: Py( 0x00007fa14bc6fef0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bbeb7b0, ), ), serializer: Enum( EnumSerializer { class: Py( 0x000055eb029bcab0, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraCenterToSelection", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraCenterToSelection", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "camera_movement", lookup_key: Simple { key: "camera_movement", py_key: Py( 0x00007fa14aef1a30, ), path: LookupPath( [ S( "camera_movement", Py( 0x00007fa14b065eb0, ), ), ], ), }, name_py: Py( 0x00007fa14bc6fef0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bbeb7b0, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb029bcab0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "vantage": 0, "none": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b38dcd0, ), Py( 0x00007fa14b38dd30, ), ], }, missing: None, expected_repr: "'vantage' or 'none'", strict: false, class_repr: "CameraMovement", name: "str-enum[CameraMovement]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[CameraMovement]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc74b0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc7690, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2ad30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_center_to_selection": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2ad30, ), ], }, expected_repr: "'default_camera_center_to_selection'", name: "literal['default_camera_center_to_selection']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_center_to_selection']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraCenterToSelection", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b995e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraCenterToSelection", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, camera_movement: kittycad.models.camera_movement.CameraMovement = 'vantage', type: Literal['default_camera_center_to_selection'] = 'default_camera_center_to_selection') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
-
camera_movement:
CameraMovement[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'camera_movement': FieldInfo(annotation=CameraMovement, required=False, default='vantage'), 'type': FieldInfo(annotation=Literal['default_camera_center_to_selection'], required=False, default='default_camera_center_to_selection')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionDefaultCameraFocusOn(**data)[source][source]
Focus the default camera upon an object in the scene.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_focus_on'], 'uuid': <class 'str'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionDefaultCameraFocusOn'>, 'config': {'title': 'OptionDefaultCameraFocusOn'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionDefaultCameraFocusOn'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionDefaultCameraFocusOn'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionDefaultCameraFocusOn:94467868096544', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_focus_on', 'schema': {'expected': ['default_camera_focus_on'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'uuid': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraFocusOn', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b87820, ), serializer: Fields( GeneralFieldsSerializer { fields: { "uuid": SerField { key_py: Py( 0x00007fa14e427de0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb7ab0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_focus_on", }, expected_py: None, name: "literal['default_camera_focus_on']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraFocusOn", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraFocusOn", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc7cf0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc7d20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb7ab0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_focus_on": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb7ab0, ), ], }, expected_repr: "'default_camera_focus_on'", name: "literal['default_camera_focus_on']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_focus_on']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "uuid", lookup_key: Simple { key: "uuid", py_key: Py( 0x00007fa14adc7d50, ), path: LookupPath( [ S( "uuid", Py( 0x00007fa14adc7d80, ), ), ], ), }, name_py: Py( 0x00007fa14e427de0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "OptionDefaultCameraFocusOn", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b87820, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraFocusOn", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['default_camera_focus_on'] = 'default_camera_focus_on', uuid: str) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['default_camera_focus_on'], required=False, default='default_camera_focus_on'), 'uuid': FieldInfo(annotation=str, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionDefaultCameraGetSettings(**data)[source][source]
Gets the default camera’s camera settings
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_get_settings']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionDefaultCameraGetSettings'>, 'config': {'title': 'OptionDefaultCameraGetSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionDefaultCameraGetSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionDefaultCameraGetSettings'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionDefaultCameraGetSettings:94467866676640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_get_settings', 'schema': {'expected': ['default_camera_get_settings'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraGetSettings', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a2cda0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2ac90, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_get_settings", }, expected_py: None, name: "literal['default_camera_get_settings']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraGetSettings", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraGetSettings", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b020750, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b020780, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2ac90, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_get_settings": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2ac90, ), ], }, expected_repr: "'default_camera_get_settings'", name: "literal['default_camera_get_settings']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_get_settings']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraGetSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a2cda0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraGetSettings", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['default_camera_get_settings'] = 'default_camera_get_settings') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['default_camera_get_settings'], required=False, default='default_camera_get_settings')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionDefaultCameraLookAt(**data)[source][source]
Change what the default camera is looking at.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'center': <class 'kittycad.models.point3d.Point3d'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'sequence': typing.Optional[int], 'type': typing.Literal['default_camera_look_at'], 'up': <class 'kittycad.models.point3d.Point3d'>, 'vantage': <class 'kittycad.models.point3d.Point3d'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.modeling_cmd.OptionDefaultCameraLookAt'>, 'config': {'title': 'OptionDefaultCameraLookAt'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionDefaultCameraLookAt'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionDefaultCameraLookAt'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionDefaultCameraLookAt:94467866691568', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'sequence': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_look_at', 'schema': {'expected': ['default_camera_look_at'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'vantage': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraLookAt', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a307f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "center": SerField { key_py: Py( 0x00007fa14ef08150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb6cf0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_look_at", }, expected_py: None, name: "literal['default_camera_look_at']", }, ), }, ), ), required: true, }, "sequence": SerField { key_py: Py( 0x00007fa14fa3b790, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Int( IntSerializer, ), }, ), }, ), ), required: true, }, "vantage": SerField { key_py: Py( 0x00007fa14bbeb7b0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "up": SerField { key_py: Py( 0x00007fa14d9ed0b0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraLookAt", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraLookAt", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007fa14afe79f0, ), path: LookupPath( [ S( "center", Py( 0x00007fa14afe78a0, ), ), ], ), }, name_py: Py( 0x00007fa14ef08150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "sequence", lookup_key: Simple { key: "sequence", py_key: Py( 0x00007fa14ae88f70, ), path: LookupPath( [ S( "sequence", Py( 0x00007fa14ae88db0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3b790, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Int( IntValidator { strict: false, }, ), name: "nullable[int]", }, ), validate_default: false, copy_default: false, name: "default[nullable[int]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe77e0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe7900, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb6cf0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_look_at": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb6cf0, ), ], }, expected_repr: "'default_camera_look_at'", name: "literal['default_camera_look_at']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_look_at']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py( 0x00007fa14afe7810, ), path: LookupPath( [ S( "up", Py( 0x00007fa14afe7870, ), ), ], ), }, name_py: Py( 0x00007fa14d9ed0b0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "vantage", lookup_key: Simple { key: "vantage", py_key: Py( 0x00007fa14afe7720, ), path: LookupPath( [ S( "vantage", Py( 0x00007fa14afe7780, ), ), ], ), }, name_py: Py( 0x00007fa14bbeb7b0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "OptionDefaultCameraLookAt", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a307f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraLookAt", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, center: kittycad.models.point3d.Point3d, sequence: Optional[int] = None, type: Literal['default_camera_look_at'] = 'default_camera_look_at', up: kittycad.models.point3d.Point3d, vantage: kittycad.models.point3d.Point3d) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'center': FieldInfo(annotation=Point3d, required=True), 'sequence': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['default_camera_look_at'], required=False, default='default_camera_look_at'), 'up': FieldInfo(annotation=Point3d, required=True), 'vantage': FieldInfo(annotation=Point3d, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionDefaultCameraPerspectiveSettings(**data)[source][source]
Change what the default camera is looking at.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'center': <class 'kittycad.models.point3d.Point3d'>, 'fov_y': typing.Optional[float], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'sequence': typing.Optional[int], 'type': typing.Literal['default_camera_perspective_settings'], 'up': <class 'kittycad.models.point3d.Point3d'>, 'vantage': <class 'kittycad.models.point3d.Point3d'>, 'z_far': typing.Optional[float], 'z_near': typing.Optional[float]}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.modeling_cmd.OptionDefaultCameraPerspectiveSettings'>, 'config': {'title': 'OptionDefaultCameraPerspectiveSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionDefaultCameraPerspectiveSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionDefaultCameraPerspectiveSettings'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionDefaultCameraPerspectiveSettings:94467866699232', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'fov_y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'sequence': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_perspective_settings', 'schema': {'expected': ['default_camera_perspective_settings'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'vantage': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'z_far': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'z_near': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraPerspectiveSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a325e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "up": SerField { key_py: Py( 0x00007fa14d9ed0b0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "fov_y": SerField { key_py: Py( 0x00007fa14bbea670, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "z_far": SerField { key_py: Py( 0x00007fa14afe4060, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "center": SerField { key_py: Py( 0x00007fa14ef08150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "vantage": SerField { key_py: Py( 0x00007fa14bbeb7b0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "z_near": SerField { key_py: Py( 0x00007fa14afe4150, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "sequence": SerField { key_py: Py( 0x00007fa14fa3b790, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Int( IntSerializer, ), }, ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2a7e0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_perspective_settings", }, expected_py: None, name: "literal['default_camera_perspective_settings']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 8, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraPerspectiveSettings", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraPerspectiveSettings", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007fa14b020c00, ), path: LookupPath( [ S( "center", Py( 0x00007fa14b020c30, ), ), ], ), }, name_py: Py( 0x00007fa14ef08150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "fov_y", lookup_key: Simple { key: "fov_y", py_key: Py( 0x00007fa14b020c60, ), path: LookupPath( [ S( "fov_y", Py( 0x00007fa14b020c90, ), ), ], ), }, name_py: Py( 0x00007fa14bbea670, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "sequence", lookup_key: Simple { key: "sequence", py_key: Py( 0x00007fa14ae964f0, ), path: LookupPath( [ S( "sequence", Py( 0x00007fa14ae964b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3b790, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Int( IntValidator { strict: false, }, ), name: "nullable[int]", }, ), validate_default: false, copy_default: false, name: "default[nullable[int]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b020cc0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b020d20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2a7e0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_perspective_settings": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2a7e0, ), ], }, expected_repr: "'default_camera_perspective_settings'", name: "literal['default_camera_perspective_settings']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_perspective_settings']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py( 0x00007fa14b020d50, ), path: LookupPath( [ S( "up", Py( 0x00007fa14b020d80, ), ), ], ), }, name_py: Py( 0x00007fa14d9ed0b0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "vantage", lookup_key: Simple { key: "vantage", py_key: Py( 0x00007fa14b020db0, ), path: LookupPath( [ S( "vantage", Py( 0x00007fa14b020de0, ), ), ], ), }, name_py: Py( 0x00007fa14bbeb7b0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "z_far", lookup_key: Simple { key: "z_far", py_key: Py( 0x00007fa14b020e10, ), path: LookupPath( [ S( "z_far", Py( 0x00007fa14b020e40, ), ), ], ), }, name_py: Py( 0x00007fa14afe4060, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "z_near", lookup_key: Simple { key: "z_near", py_key: Py( 0x00007fa14b020e70, ), path: LookupPath( [ S( "z_near", Py( 0x00007fa14b020ea0, ), ), ], ), }, name_py: Py( 0x00007fa14afe4150, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraPerspectiveSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a325e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraPerspectiveSettings", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, center: kittycad.models.point3d.Point3d, fov_y: Optional[float] = None, sequence: Optional[int] = None, type: Literal['default_camera_perspective_settings'] = 'default_camera_perspective_settings', up: kittycad.models.point3d.Point3d, vantage: kittycad.models.point3d.Point3d, z_far: Optional[float] = None, z_near: Optional[float] = None) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'center': FieldInfo(annotation=Point3d, required=True), 'fov_y': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'sequence': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['default_camera_perspective_settings'], required=False, default='default_camera_perspective_settings'), 'up': FieldInfo(annotation=Point3d, required=True), 'vantage': FieldInfo(annotation=Point3d, required=True), 'z_far': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'z_near': FieldInfo(annotation=Union[float, NoneType], required=False, default=None)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionDefaultCameraSetOrthographic(**data)[source][source]
Use orthographic projection.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_set_orthographic']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionDefaultCameraSetOrthographic'>, 'config': {'title': 'OptionDefaultCameraSetOrthographic'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionDefaultCameraSetOrthographic'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionDefaultCameraSetOrthographic'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionDefaultCameraSetOrthographic:94467868116672', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_set_orthographic', 'schema': {'expected': ['default_camera_set_orthographic'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraSetOrthographic', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b8c6c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2a6f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_set_orthographic", }, expected_py: None, name: "literal['default_camera_set_orthographic']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraSetOrthographic", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraSetOrthographic", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad57180, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad56c10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2a6f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_set_orthographic": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2a6f0, ), ], }, expected_repr: "'default_camera_set_orthographic'", name: "literal['default_camera_set_orthographic']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_set_orthographic']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraSetOrthographic", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b8c6c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraSetOrthographic", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['default_camera_set_orthographic'] = 'default_camera_set_orthographic') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['default_camera_set_orthographic'], required=False, default='default_camera_set_orthographic')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionDefaultCameraSetPerspective(**data)[source][source]
Use perspective projection.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'parameters': typing.Optional[kittycad.models.perspective_camera_parameters.PerspectiveCameraParameters], 'type': typing.Literal['default_camera_set_perspective']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionDefaultCameraSetPerspective'>, 'config': {'title': 'OptionDefaultCameraSetPerspective'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionDefaultCameraSetPerspective'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionDefaultCameraSetPerspective'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionDefaultCameraSetPerspective:94467868161744', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'parameters': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.perspective_camera_parameters.PerspectiveCameraParameters'>, 'config': {'title': 'PerspectiveCameraParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.perspective_camera_parameters.PerspectiveCameraParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.perspective_camera_parameters.PerspectiveCameraParameters'>>]}, 'ref': 'kittycad.models.perspective_camera_parameters.PerspectiveCameraParameters:94467866251984', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'fov_y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'z_far': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'z_near': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'PerspectiveCameraParameters', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_set_perspective', 'schema': {'expected': ['default_camera_set_perspective'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraSetPerspective', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b976d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "parameters": SerField { key_py: Py( 0x00007fa14ea42f70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055eb039c52d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "fov_y": SerField { key_py: Py( 0x00007fa14bbea670, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "z_near": SerField { key_py: Py( 0x00007fa14afe4150, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "z_far": SerField { key_py: Py( 0x00007fa14afe4060, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "PerspectiveCameraParameters", }, ), }, ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2a650, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_set_perspective", }, expected_py: None, name: "literal['default_camera_set_perspective']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraSetPerspective", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraSetPerspective", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "parameters", lookup_key: Simple { key: "parameters", py_key: Py( 0x00007fa14ae047b0, ), path: LookupPath( [ S( "parameters", Py( 0x00007fa14ae04770, ), ), ], ), }, name_py: Py( 0x00007fa14ea42f70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "fov_y", lookup_key: Simple { key: "fov_y", py_key: Py( 0x00007fa14ae006c0, ), path: LookupPath( [ S( "fov_y", Py( 0x00007fa14ae006f0, ), ), ], ), }, name_py: Py( 0x00007fa14bbea670, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "z_far", lookup_key: Simple { key: "z_far", py_key: Py( 0x00007fa14ae00720, ), path: LookupPath( [ S( "z_far", Py( 0x00007fa14ae00750, ), ), ], ), }, name_py: Py( 0x00007fa14afe4060, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "z_near", lookup_key: Simple { key: "z_near", py_key: Py( 0x00007fa14ae00780, ), path: LookupPath( [ S( "z_near", Py( 0x00007fa14ae007b0, ), ), ], ), }, name_py: Py( 0x00007fa14afe4150, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "PerspectiveCameraParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039c52d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "PerspectiveCameraParameters", }, ), name: "nullable[PerspectiveCameraParameters]", }, ), validate_default: false, copy_default: false, name: "default[nullable[PerspectiveCameraParameters]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae007e0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae00810, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2a650, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_set_perspective": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2a650, ), ], }, expected_repr: "'default_camera_set_perspective'", name: "literal['default_camera_set_perspective']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_set_perspective']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraSetPerspective", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b976d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraSetPerspective", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, parameters: Optional[kittycad.models.perspective_camera_parameters.PerspectiveCameraParameters] = None, type: Literal['default_camera_set_perspective'] = 'default_camera_set_perspective') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'parameters': FieldInfo(annotation=Union[PerspectiveCameraParameters, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['default_camera_set_perspective'], required=False, default='default_camera_set_perspective')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
-
parameters:
Optional[PerspectiveCameraParameters][source]
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionDefaultCameraZoom(**data)[source][source]
Adjust zoom of the default camera.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'magnitude': <class 'float'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_zoom']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionDefaultCameraZoom'>, 'config': {'title': 'OptionDefaultCameraZoom'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionDefaultCameraZoom'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionDefaultCameraZoom'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionDefaultCameraZoom:94467866719728', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'magnitude': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_zoom', 'schema': {'expected': ['default_camera_zoom'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraZoom', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a375f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "magnitude": SerField { key_py: Py( 0x00007fa14d8610f0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb68b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_zoom", }, expected_py: None, name: "literal['default_camera_zoom']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraZoom", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraZoom", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "magnitude", lookup_key: Simple { key: "magnitude", py_key: Py( 0x00007fa14ae970f0, ), path: LookupPath( [ S( "magnitude", Py( 0x00007fa14ae970b0, ), ), ], ), }, name_py: Py( 0x00007fa14d8610f0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b0213e0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b021410, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb68b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_zoom": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb68b0, ), ], }, expected_repr: "'default_camera_zoom'", name: "literal['default_camera_zoom']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_zoom']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraZoom", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a375f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraZoom", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, magnitude: float, type: Literal['default_camera_zoom'] = 'default_camera_zoom') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'magnitude': FieldInfo(annotation=float, required=True), 'type': FieldInfo(annotation=Literal['default_camera_zoom'], required=False, default='default_camera_zoom')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionDensity(**data)[source][source]
Get the density of entities in the scene or the default scene.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entity_ids': typing.List[str], 'material_mass': <class 'float'>, 'material_mass_unit': <enum 'UnitMass'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'output_unit': <enum 'UnitDensity'>, 'type': typing.Literal['density']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionDensity'>, 'config': {'title': 'OptionDensity'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionDensity'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionDensity'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionDensity:94467868045984', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'material_mass': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'material_mass_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitMass'>, 'members': [UnitMass.G, UnitMass.KG, UnitMass.LB], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_mass.UnitMass:94467863219280', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitDensity'>, 'members': [UnitDensity.LB_FT3, UnitDensity.KG_M3], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_density.UnitDensity:94467863217504', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'density', 'schema': {'expected': ['density'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDensity', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b7b2a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14cbce4c0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "density", }, expected_py: None, name: "literal['density']", }, ), }, ), ), required: true, }, "entity_ids": SerField { key_py: Py( 0x00007fa14b038970, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "output_unit": SerField { key_py: Py( 0x00007fa14bb4f470, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb036e0560, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "material_mass": SerField { key_py: Py( 0x00007fa14bbd5030, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "material_mass_unit": SerField { key_py: Py( 0x00007fa14bbd4f30, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb036e0c50, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "OptionDensity", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDensity", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007fa14adcbcf0, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007fa14adca730, ), ), ], ), }, name_py: Py( 0x00007fa14b038970, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "material_mass", lookup_key: Simple { key: "material_mass", py_key: Py( 0x00007fa14adcb370, ), path: LookupPath( [ S( "material_mass", Py( 0x00007fa14adcb570, ), ), ], ), }, name_py: Py( 0x00007fa14bbd5030, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "material_mass_unit", lookup_key: Simple { key: "material_mass_unit", py_key: Py( 0x00007fa14adcbeb0, ), path: LookupPath( [ S( "material_mass_unit", Py( 0x00007fa14adcbc30, ), ), ], ), }, name_py: Py( 0x00007fa14bbd4f30, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb036e0c50, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "g": 0, "lb": 2, "kg": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c7dd0, ), Py( 0x00007fa14b3c7e30, ), Py( 0x00007fa14b3c7e90, ), ], }, missing: None, expected_repr: "'g', 'kg' or 'lb'", strict: false, class_repr: "UnitMass", name: "str-enum[UnitMass]", }, ), frozen: false, }, Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007fa14adcb670, ), path: LookupPath( [ S( "output_unit", Py( 0x00007fa14adcba30, ), ), ], ), }, name_py: Py( 0x00007fa14bb4f470, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb036e0560, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "kg:m3": 1, "lb:ft3": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c7cb0, ), Py( 0x00007fa14b3c7d10, ), ], }, missing: None, expected_repr: "'lb:ft3' or 'kg:m3'", strict: false, class_repr: "UnitDensity", name: "str-enum[UnitDensity]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc62b0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc6160, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14cbce4c0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "density": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14cbce4c0, ), ], }, expected_repr: "'density'", name: "literal['density']", }, ), validate_default: false, copy_default: false, name: "default[literal['density']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDensity", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b7b2a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDensity", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entity_ids: List[str], material_mass: float, material_mass_unit: kittycad.models.unit_mass.UnitMass, output_unit: kittycad.models.unit_density.UnitDensity, type: Literal['density'] = 'density') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entity_ids': FieldInfo(annotation=List[str], required=True), 'material_mass': FieldInfo(annotation=float, required=True), 'material_mass_unit': FieldInfo(annotation=UnitMass, required=True), 'output_unit': FieldInfo(annotation=UnitDensity, required=True), 'type': FieldInfo(annotation=Literal['density'], required=False, default='density')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
-
output_unit:
UnitDensity[source]
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionDisableDryRun(**data)[source][source]
Sets whether or not changes to the scene or its objects will be done as a “dry run” In a dry run, successful commands won’t actually change the model. This is useful for catching errors before actually making the change.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['disable_dry_run']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionDisableDryRun'>, 'config': {'title': 'OptionDisableDryRun'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionDisableDryRun'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionDisableDryRun'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionDisableDryRun:94467867728720', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'disable_dry_run', 'schema': {'expected': ['disable_dry_run'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDisableDryRun', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b2db50, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb6c30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "disable_dry_run", }, expected_py: None, name: "literal['disable_dry_run']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionDisableDryRun", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDisableDryRun", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad55920, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad55950, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb6c30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "disable_dry_run": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb6c30, ), ], }, expected_repr: "'disable_dry_run'", name: "literal['disable_dry_run']", }, ), validate_default: false, copy_default: false, name: "default[literal['disable_dry_run']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDisableDryRun", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b2db50, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDisableDryRun", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['disable_dry_run'] = 'disable_dry_run') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['disable_dry_run'], required=False, default='disable_dry_run')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEdgeLinesVisible(**data)[source][source]
Changes visibility of scene-wide edge lines on brep solids
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'hidden': <class 'bool'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['edge_lines_visible']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEdgeLinesVisible'>, 'config': {'title': 'OptionEdgeLinesVisible'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEdgeLinesVisible'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEdgeLinesVisible'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEdgeLinesVisible:94467867301200', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'hidden': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'edge_lines_visible', 'schema': {'expected': ['edge_lines_visible'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEdgeLinesVisible', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ac5550, ), serializer: Fields( GeneralFieldsSerializer { fields: { "hidden": SerField { key_py: Py( 0x00007fa14e0bbea0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb6f70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "edge_lines_visible", }, expected_py: None, name: "literal['edge_lines_visible']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEdgeLinesVisible", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEdgeLinesVisible", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "hidden", lookup_key: Simple { key: "hidden", py_key: Py( 0x00007fa14b026850, ), path: LookupPath( [ S( "hidden", Py( 0x00007fa14b026880, ), ), ], ), }, name_py: Py( 0x00007fa14e0bbea0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b0268b0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b0268e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb6f70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "edge_lines_visible": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb6f70, ), ], }, expected_repr: "'edge_lines_visible'", name: "literal['edge_lines_visible']", }, ), validate_default: false, copy_default: false, name: "default[literal['edge_lines_visible']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEdgeLinesVisible", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ac5550, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEdgeLinesVisible", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, hidden: bool, type: Literal['edge_lines_visible'] = 'edge_lines_visible') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'hidden': FieldInfo(annotation=bool, required=True), 'type': FieldInfo(annotation=Literal['edge_lines_visible'], required=False, default='edge_lines_visible')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEnableDryRun(**data)[source][source]
Sets whether or not changes to the scene or its objects will be done as a “dry run” In a dry run, successful commands won’t actually change the model. This is useful for catching errors before actually making the change.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['enable_dry_run']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEnableDryRun'>, 'config': {'title': 'OptionEnableDryRun'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEnableDryRun'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEnableDryRun'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEnableDryRun:94467867709344', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'enable_dry_run', 'schema': {'expected': ['enable_dry_run'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEnableDryRun', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b28fa0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be49730, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "enable_dry_run", }, expected_py: None, name: "literal['enable_dry_run']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionEnableDryRun", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEnableDryRun", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad55440, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad55470, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be49730, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "enable_dry_run": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be49730, ), ], }, expected_repr: "'enable_dry_run'", name: "literal['enable_dry_run']", }, ), validate_default: false, copy_default: false, name: "default[literal['enable_dry_run']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEnableDryRun", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b28fa0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEnableDryRun", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['enable_dry_run'] = 'enable_dry_run') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['enable_dry_run'], required=False, default='enable_dry_run')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEnableSketchMode(**data)[source][source]
Sketch on some entity (e.g. a plane, a face).
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'adjust_camera': <class 'bool'>, 'animated': <class 'bool'>, 'entity_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'ortho': <class 'bool'>, 'planar_normal': typing.Optional[kittycad.models.point3d.Point3d], 'type': typing.Literal['enable_sketch_mode']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEnableSketchMode'>, 'config': {'title': 'OptionEnableSketchMode'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEnableSketchMode'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEnableSketchMode'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEnableSketchMode:94467867696192', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'adjust_camera': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'animated': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'ortho': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'planar_normal': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'enable_sketch_mode', 'schema': {'expected': ['enable_sketch_mode'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEnableSketchMode', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b25c40, ), serializer: Fields( GeneralFieldsSerializer { fields: { "ortho": SerField { key_py: Py( 0x00007fa14bbea7c0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "planar_normal": SerField { key_py: Py( 0x00007fa14b01ecb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055eb0330e750, ), serializer: Fields( GeneralFieldsSerializer { fields: { "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), }, ), }, ), ), required: true, }, "adjust_camera": SerField { key_py: Py( 0x00007fa14b01ebf0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "animated": SerField { key_py: Py( 0x00007fa14b01ec30, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4c070, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "enable_sketch_mode", }, expected_py: None, name: "literal['enable_sketch_mode']", }, ), }, ), ), required: true, }, "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 6, }, ), has_extra: false, root_model: false, name: "OptionEnableSketchMode", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEnableSketchMode", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "adjust_camera", lookup_key: Simple { key: "adjust_camera", py_key: Py( 0x00007fa14ad92370, ), path: LookupPath( [ S( "adjust_camera", Py( 0x00007fa14ad92330, ), ), ], ), }, name_py: Py( 0x00007fa14b01ebf0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "animated", lookup_key: Simple { key: "animated", py_key: Py( 0x00007fa14ad923f0, ), path: LookupPath( [ S( "animated", Py( 0x00007fa14ad923b0, ), ), ], ), }, name_py: Py( 0x00007fa14b01ec30, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14ad92430, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14ad92470, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "ortho", lookup_key: Simple { key: "ortho", py_key: Py( 0x00007fa14ad54690, ), path: LookupPath( [ S( "ortho", Py( 0x00007fa14ad546c0, ), ), ], ), }, name_py: Py( 0x00007fa14bbea7c0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "planar_normal", lookup_key: Simple { key: "planar_normal", py_key: Py( 0x00007fa14ad924b0, ), path: LookupPath( [ S( "planar_normal", Py( 0x00007fa14ad924f0, ), ), ], ), }, name_py: Py( 0x00007fa14b01ecb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0330e750, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point3d", }, ), name: "nullable[Point3d]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Point3d]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad54150, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad547b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4c070, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "enable_sketch_mode": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4c070, ), ], }, expected_repr: "'enable_sketch_mode'", name: "literal['enable_sketch_mode']", }, ), validate_default: false, copy_default: false, name: "default[literal['enable_sketch_mode']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEnableSketchMode", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b25c40, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEnableSketchMode", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, adjust_camera: bool, animated: bool, entity_id: str, ortho: bool, planar_normal: Optional[kittycad.models.point3d.Point3d] = None, type: Literal['enable_sketch_mode'] = 'enable_sketch_mode') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'adjust_camera': FieldInfo(annotation=bool, required=True), 'animated': FieldInfo(annotation=bool, required=True), 'entity_id': FieldInfo(annotation=str, required=True), 'ortho': FieldInfo(annotation=bool, required=True), 'planar_normal': FieldInfo(annotation=Union[Point3d, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['enable_sketch_mode'], required=False, default='enable_sketch_mode')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEngineUtilEvaluatePath(**data)[source][source]
Evaluates the position of a path in one shot (engine utility for kcl executor)
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'path_json': <class 'str'>, 't': <class 'float'>, 'type': typing.Literal['engine_util_evaluate_path']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEngineUtilEvaluatePath'>, 'config': {'title': 'OptionEngineUtilEvaluatePath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEngineUtilEvaluatePath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEngineUtilEvaluatePath'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEngineUtilEvaluatePath:94467866432160', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'path_json': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 't': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'engine_util_evaluate_path', 'schema': {'expected': ['engine_util_evaluate_path'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEngineUtilEvaluatePath', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb039f12a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "t": SerField { key_py: Py( 0x00007fa14fa3e080, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "path_json": SerField { key_py: Py( 0x00007fa14afd7ef0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2a380, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "engine_util_evaluate_path", }, expected_py: None, name: "literal['engine_util_evaluate_path']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionEngineUtilEvaluatePath", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEngineUtilEvaluatePath", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "path_json", lookup_key: Simple { key: "path_json", py_key: Py( 0x00007fa14af39c30, ), path: LookupPath( [ S( "path_json", Py( 0x00007fa14af3aeb0, ), ), ], ), }, name_py: Py( 0x00007fa14afd7ef0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "t", lookup_key: Simple { key: "t", py_key: Py( 0x00007fa14fa3e080, ), path: LookupPath( [ S( "t", Py( 0x00007fa14fa3e080, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e080, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afd3d20, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afd3cf0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2a380, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "engine_util_evaluate_path": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2a380, ), ], }, expected_repr: "'engine_util_evaluate_path'", name: "literal['engine_util_evaluate_path']", }, ), validate_default: false, copy_default: false, name: "default[literal['engine_util_evaluate_path']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEngineUtilEvaluatePath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039f12a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEngineUtilEvaluatePath", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, path_json: str, t: float, type: Literal['engine_util_evaluate_path'] = 'engine_util_evaluate_path') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'path_json': FieldInfo(annotation=str, required=True), 't': FieldInfo(annotation=float, required=True), 'type': FieldInfo(annotation=Literal['engine_util_evaluate_path'], required=False, default='engine_util_evaluate_path')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEntityCircularPattern(**data)[source][source]
Create a circular pattern using this entity.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'arc_degrees': <class 'float'>, 'axis': <class 'kittycad.models.point3d.Point3d'>, 'center': <class 'kittycad.models.point3d.Point3d'>, 'entity_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'num_repetitions': <class 'int'>, 'rotate_duplicates': <class 'bool'>, 'type': typing.Literal['entity_circular_pattern']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.modeling_cmd.OptionEntityCircularPattern'>, 'config': {'title': 'OptionEntityCircularPattern'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEntityCircularPattern'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEntityCircularPattern'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEntityCircularPattern:94467861889984', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'arc_degrees': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'num_repetitions': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}, 'rotate_duplicates': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_circular_pattern', 'schema': {'expected': ['entity_circular_pattern'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityCircularPattern', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb0359c3c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4d670, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_circular_pattern", }, expected_py: None, name: "literal['entity_circular_pattern']", }, ), }, ), ), required: true, }, "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "rotate_duplicates": SerField { key_py: Py( 0x00007fa14aff66f0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "center": SerField { key_py: Py( 0x00007fa14ef08150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "axis": SerField { key_py: Py( 0x00007fa14c13dce0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "num_repetitions": SerField { key_py: Py( 0x00007fa14aff6470, ), alias: None, alias_py: None, serializer: Some( Int( IntSerializer, ), ), required: true, }, "arc_degrees": SerField { key_py: Py( 0x00007fa14aff6630, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "OptionEntityCircularPattern", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityCircularPattern", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "arc_degrees", lookup_key: Simple { key: "arc_degrees", py_key: Py( 0x00007fa14d4a7d70, ), path: LookupPath( [ S( "arc_degrees", Py( 0x00007fa14cb9d1f0, ), ), ], ), }, name_py: Py( 0x00007fa14aff6630, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "axis", lookup_key: Simple { key: "axis", py_key: Py( 0x00007fa14b023e70, ), path: LookupPath( [ S( "axis", Py( 0x00007fa14b023ed0, ), ), ], ), }, name_py: Py( 0x00007fa14c13dce0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007fa14b023750, ), path: LookupPath( [ S( "center", Py( 0x00007fa14b0237e0, ), ), ], ), }, name_py: Py( 0x00007fa14ef08150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14cb9fd30, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14d53efb0, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "num_repetitions", lookup_key: Simple { key: "num_repetitions", py_key: Py( 0x00007fa14d53e0f0, ), path: LookupPath( [ S( "num_repetitions", Py( 0x00007fa14cd14830, ), ), ], ), }, name_py: Py( 0x00007fa14aff6470, ), validator: Int( IntValidator { strict: false, }, ), frozen: false, }, Field { name: "rotate_duplicates", lookup_key: Simple { key: "rotate_duplicates", py_key: Py( 0x00007fa14c725d30, ), path: LookupPath( [ S( "rotate_duplicates", Py( 0x00007fa14c725530, ), ), ], ), }, name_py: Py( 0x00007fa14aff66f0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b023630, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b023660, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4d670, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_circular_pattern": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4d670, ), ], }, expected_repr: "'entity_circular_pattern'", name: "literal['entity_circular_pattern']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_circular_pattern']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityCircularPattern", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0359c3c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityCircularPattern", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, arc_degrees: float, axis: kittycad.models.point3d.Point3d, center: kittycad.models.point3d.Point3d, entity_id: str, num_repetitions: int, rotate_duplicates: bool, type: Literal['entity_circular_pattern'] = 'entity_circular_pattern') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'arc_degrees': FieldInfo(annotation=float, required=True), 'axis': FieldInfo(annotation=Point3d, required=True), 'center': FieldInfo(annotation=Point3d, required=True), 'entity_id': FieldInfo(annotation=str, required=True), 'num_repetitions': FieldInfo(annotation=int, required=True), 'rotate_duplicates': FieldInfo(annotation=bool, required=True), 'type': FieldInfo(annotation=Literal['entity_circular_pattern'], required=False, default='entity_circular_pattern')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEntityFade(**data)[source][source]
Fade entity in or out.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'duration_seconds': <class 'float'>, 'entity_id': <class 'str'>, 'fade_in': <class 'bool'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_fade']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEntityFade'>, 'config': {'title': 'OptionEntityFade'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEntityFade'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEntityFade'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEntityFade:94467867582384', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'duration_seconds': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 0.4000000059604645, 'schema': {'type': 'float'}, 'type': 'default'}, 'type': 'model-field'}, 'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'fade_in': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_fade', 'schema': {'expected': ['entity_fade'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityFade', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b09fb0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "duration_seconds": SerField { key_py: Py( 0x00007fa14b01dc30, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14b0f4db0, ), ), serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), ), required: true, }, "fade_in": SerField { key_py: Py( 0x00007fa14b021ad0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4d730, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_fade", }, expected_py: None, name: "literal['entity_fade']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "OptionEntityFade", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityFade", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "duration_seconds", lookup_key: Simple { key: "duration_seconds", py_key: Py( 0x00007fa14b1dedb0, ), path: LookupPath( [ S( "duration_seconds", Py( 0x00007fa14ad4a2f0, ), ), ], ), }, name_py: Py( 0x00007fa14b01dc30, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14b0f4db0, ), ), on_error: Raise, validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), validate_default: false, copy_default: false, name: "default[float]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14ad4a370, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14ad4a330, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "fade_in", lookup_key: Simple { key: "fade_in", py_key: Py( 0x00007fa14ae756e0, ), path: LookupPath( [ S( "fade_in", Py( 0x00007fa14ae75680, ), ), ], ), }, name_py: Py( 0x00007fa14b021ad0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae757d0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae75aa0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4d730, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_fade": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4d730, ), ], }, expected_repr: "'entity_fade'", name: "literal['entity_fade']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_fade']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityFade", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b09fb0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityFade", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, duration_seconds: float = 0.4000000059604645, entity_id: str, fade_in: bool, type: Literal['entity_fade'] = 'entity_fade') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'duration_seconds': FieldInfo(annotation=float, required=False, default=0.4000000059604645), 'entity_id': FieldInfo(annotation=str, required=True), 'fade_in': FieldInfo(annotation=bool, required=True), 'type': FieldInfo(annotation=Literal['entity_fade'], required=False, default='entity_fade')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEntityGetAllChildUuids(**data)[source][source]
What are all UUIDs of this entity’s children?
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entity_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_all_child_uuids']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEntityGetAllChildUuids'>, 'config': {'title': 'OptionEntityGetAllChildUuids'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEntityGetAllChildUuids'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEntityGetAllChildUuids'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEntityGetAllChildUuids:94467866903664', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_all_child_uuids', 'schema': {'expected': ['entity_get_all_child_uuids'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetAllChildUuids', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a64470, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2a240, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_all_child_uuids", }, expected_py: None, name: "literal['entity_get_all_child_uuids']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetAllChildUuids", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetAllChildUuids", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14b09b1f0, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14aeb88b0, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b023360, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b023390, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2a240, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_all_child_uuids": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2a240, ), ], }, expected_repr: "'entity_get_all_child_uuids'", name: "literal['entity_get_all_child_uuids']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_all_child_uuids']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetAllChildUuids", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a64470, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityGetAllChildUuids", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entity_id: str, type: Literal['entity_get_all_child_uuids'] = 'entity_get_all_child_uuids') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entity_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['entity_get_all_child_uuids'], required=False, default='entity_get_all_child_uuids')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEntityGetChildUuid(**data)[source][source]
What is the UUID of this entity’s n-th child?
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'child_index': <class 'int'>, 'entity_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_child_uuid']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEntityGetChildUuid'>, 'config': {'title': 'OptionEntityGetChildUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEntityGetChildUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEntityGetChildUuid'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEntityGetChildUuid:94467866895040', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'child_index': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}, 'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_child_uuid', 'schema': {'expected': ['entity_get_child_uuid'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetChildUuid', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a622c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4d8b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_child_uuid", }, expected_py: None, name: "literal['entity_get_child_uuid']", }, ), }, ), ), required: true, }, "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "child_index": SerField { key_py: Py( 0x00007fa14aff5db0, ), alias: None, alias_py: None, serializer: Some( Int( IntSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionEntityGetChildUuid", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetChildUuid", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "child_index", lookup_key: Simple { key: "child_index", py_key: Py( 0x00007fa14af93870, ), path: LookupPath( [ S( "child_index", Py( 0x00007fa14aeaf7f0, ), ), ], ), }, name_py: Py( 0x00007fa14aff5db0, ), validator: Int( IntValidator { strict: false, }, ), frozen: false, }, Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14aeaf870, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14aeaf830, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b022df0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b022e20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4d8b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_child_uuid": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4d8b0, ), ], }, expected_repr: "'entity_get_child_uuid'", name: "literal['entity_get_child_uuid']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_child_uuid']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetChildUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a622c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityGetChildUuid", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, child_index: int, entity_id: str, type: Literal['entity_get_child_uuid'] = 'entity_get_child_uuid') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'child_index': FieldInfo(annotation=int, required=True), 'entity_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['entity_get_child_uuid'], required=False, default='entity_get_child_uuid')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEntityGetDistance(**data)[source][source]
What is the distance between these two entities?
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'distance_type': <class 'pydantic.root_model.RootModel[Annotated[Union[OptionEuclidean, OptionOnAxis], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, 'entity_id1': <class 'str'>, 'entity_id2': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_distance']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEntityGetDistance'>, 'config': {'title': 'OptionEntityGetDistance'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEntityGetDistance'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEntityGetDistance'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEntityGetDistance:94467866921600', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'distance_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'pydantic.root_model.RootModel[Annotated[Union[OptionEuclidean, OptionOnAxis], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, 'config': {'title': "RootModel[Annotated[Union[OptionEuclidean, OptionOnAxis], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]"}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'pydantic.root_model.RootModel[Annotated[Union[OptionEuclidean, OptionOnAxis], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'pydantic.root_model.RootModel[Annotated[Union[OptionEuclidean, OptionOnAxis], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>>]}, 'ref': "pydantic.root_model.RootModel:94467864067568[Annotated[Union[OptionEuclidean, OptionOnAxis], FieldInfo(annotation=NoneType, required=True, discriminator='type')]:140330727444880]", 'root_model': True, 'schema': {'choices': {'euclidean': {'cls': <class 'kittycad.models.distance_type.OptionEuclidean'>, 'config': {'title': 'OptionEuclidean'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.distance_type.OptionEuclidean'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.distance_type.OptionEuclidean'>>]}, 'ref': 'kittycad.models.distance_type.OptionEuclidean:94467864444368', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'euclidean', 'schema': {'expected': ['euclidean'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEuclidean', 'type': 'model-fields'}, 'type': 'model'}, 'on_axis': {'cls': <class 'kittycad.models.distance_type.OptionOnAxis'>, 'config': {'title': 'OptionOnAxis'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.distance_type.OptionOnAxis'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.distance_type.OptionOnAxis'>>]}, 'ref': 'kittycad.models.distance_type.OptionOnAxis:94467864425568', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'GlobalAxis'>, 'members': [GlobalAxis.X, GlobalAxis.Y, GlobalAxis.Z], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.global_axis.GlobalAxis:94467864441808', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'on_axis', 'schema': {'expected': ['on_axis'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionOnAxis', 'type': 'model-fields'}, 'type': 'model'}}, 'discriminator': 'type', 'from_attributes': True, 'metadata': {}, 'strict': False, 'type': 'tagged-union'}, 'type': 'model'}, 'type': 'model-field'}, 'entity_id1': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'entity_id2': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_distance', 'schema': {'expected': ['entity_get_distance'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetDistance', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a68a80, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4d9b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_distance", }, expected_py: None, name: "literal['entity_get_distance']", }, ), }, ), ), required: true, }, "entity_id2": SerField { key_py: Py( 0x00007fa14aff6270, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "entity_id1": SerField { key_py: Py( 0x00007fa14aff6230, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "distance_type": SerField { key_py: Py( 0x00007fa14beb6b70, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0380ced0, ), serializer: TaggedUnion( TaggedUnionSerializer { discriminator: LookupKey( Simple { key: "type", py_key: Py( 0x00007fa14fa3c5a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14fa3c5a0, ), ), ], ), }, ), lookup: { "on_axis": 1, "euclidean": 0, }, choices: [ Model( ModelSerializer { class: Py( 0x000055eb0380bdd0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14b2158f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "euclidean", }, expected_py: None, name: "literal['euclidean']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionEuclidean", }, ), Model( ModelSerializer { class: Py( 0x000055eb03807460, ), serializer: Fields( GeneralFieldsSerializer { fields: { "axis": SerField { key_py: Py( 0x00007fa14c13dce0, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb0380b3d0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14b18afa0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "on_axis", }, expected_py: None, name: "literal['on_axis']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionOnAxis", }, ), ], name: "TaggedUnion[OptionEuclidean, OptionOnAxis]", }, ), has_extra: false, root_model: true, name: "RootModel[Annotated[Union[OptionEuclidean, OptionOnAxis], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "OptionEntityGetDistance", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetDistance", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "distance_type", lookup_key: Simple { key: "distance_type", py_key: Py( 0x00007fa14ae95730, ), path: LookupPath( [ S( "distance_type", Py( 0x00007fa14ae94a70, ), ), ], ), }, name_py: Py( 0x00007fa14beb6b70, ), validator: Model( ModelValidator { revalidate: Never, validator: TaggedUnion( TaggedUnionValidator { discriminator: LookupKey( Simple { key: "type", py_key: Py( 0x00007fa14fa3c5a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14fa3c5a0, ), ), ], ), }, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "euclidean": 0, "on_axis": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b022190, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b022220, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14b2158f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "euclidean": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b2158f0, ), ], }, expected_repr: "'euclidean'", name: "literal['euclidean']", }, ), validate_default: false, copy_default: false, name: "default[literal['euclidean']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEuclidean", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0380bdd0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEuclidean", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "axis", lookup_key: Simple { key: "axis", py_key: Py( 0x00007fa14b022250, ), path: LookupPath( [ S( "axis", Py( 0x00007fa14b022070, ), ), ], ), }, name_py: Py( 0x00007fa14c13dce0, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb0380b3d0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "z": 2, "y": 1, "x": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b1a2e70, ), Py( 0x00007fa14b1a2ed0, ), Py( 0x00007fa14b1a2f30, ), ], }, missing: None, expected_repr: "'x', 'y' or 'z'", strict: false, class_repr: "GlobalAxis", name: "str-enum[GlobalAxis]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b021fb0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b021f80, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14b18afa0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "on_axis": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b18afa0, ), ], }, expected_repr: "'on_axis'", name: "literal['on_axis']", }, ), validate_default: false, copy_default: false, name: "default[literal['on_axis']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionOnAxis", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03807460, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionOnAxis", }, ), ], }, from_attributes: true, strict: false, custom_error: None, tags_repr: "'euclidean', 'on_axis'", discriminator_repr: "'type'", name: "tagged-union[OptionEuclidean,OptionOnAxis]", }, ), class: Py( 0x000055eb0380ced0, ), post_init: None, frozen: false, custom_init: false, root_model: true, undefined: Py( 0x00007fa14d802350, ), name: "RootModel[Annotated[Union[OptionEuclidean, OptionOnAxis], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]", }, ), frozen: false, }, Field { name: "entity_id1", lookup_key: Simple { key: "entity_id1", py_key: Py( 0x00007fa14ae956b0, ), path: LookupPath( [ S( "entity_id1", Py( 0x00007fa14ae948b0, ), ), ], ), }, name_py: Py( 0x00007fa14aff6230, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "entity_id2", lookup_key: Simple { key: "entity_id2", py_key: Py( 0x00007fa14ae95130, ), path: LookupPath( [ S( "entity_id2", Py( 0x00007fa14ae952b0, ), ), ], ), }, name_py: Py( 0x00007fa14aff6270, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b021f50, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b022040, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4d9b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_distance": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4d9b0, ), ], }, expected_repr: "'entity_get_distance'", name: "literal['entity_get_distance']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_distance']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetDistance", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a68a80, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityGetDistance", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, distance_type: pydantic.root_model.RootModel[Annotated[Union[OptionEuclidean, OptionOnAxis], FieldInfo(annotation=NoneType, required=True, discriminator='type')]], entity_id1: str, entity_id2: str, type: Literal['entity_get_distance'] = 'entity_get_distance') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
-
distance_type:
RootModel[Annotated[Union[OptionEuclidean, OptionOnAxis], FieldInfo(annotation=NoneType, required=True, discriminator='type')]][source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'distance_type': FieldInfo(annotation=RootModel[Annotated[Union[OptionEuclidean, OptionOnAxis], FieldInfo(annotation=NoneType, required=True, discriminator='type')]], required=True), 'entity_id1': FieldInfo(annotation=str, required=True), 'entity_id2': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['entity_get_distance'], required=False, default='entity_get_distance')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEntityGetNumChildren(**data)[source][source]
How many children does the entity have?
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entity_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_num_children']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEntityGetNumChildren'>, 'config': {'title': 'OptionEntityGetNumChildren'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEntityGetNumChildren'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEntityGetNumChildren'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEntityGetNumChildren:94467866886576', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_num_children', 'schema': {'expected': ['entity_get_num_children'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetNumChildren', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a601b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4dab0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_num_children", }, expected_py: None, name: "literal['entity_get_num_children']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetNumChildren", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetNumChildren", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14aeae7b0, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14aeae670, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b022790, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b022850, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4dab0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_num_children": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4dab0, ), ], }, expected_repr: "'entity_get_num_children'", name: "literal['entity_get_num_children']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_num_children']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetNumChildren", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a601b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityGetNumChildren", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entity_id: str, type: Literal['entity_get_num_children'] = 'entity_get_num_children') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entity_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['entity_get_num_children'], required=False, default='entity_get_num_children')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEntityGetParentId(**data)[source][source]
What is this entity’s parent?
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entity_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_parent_id']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEntityGetParentId'>, 'config': {'title': 'OptionEntityGetParentId'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEntityGetParentId'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEntityGetParentId'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEntityGetParentId:94467866758656', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_parent_id', 'schema': {'expected': ['entity_get_parent_id'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetParentId', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a40e00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4dbb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_parent_id", }, expected_py: None, name: "literal['entity_get_parent_id']", }, ), }, ), ), required: true, }, "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetParentId", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetParentId", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14ae95430, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14ae97b30, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b0222b0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b0222e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4dbb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_parent_id": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4dbb0, ), ], }, expected_repr: "'entity_get_parent_id'", name: "literal['entity_get_parent_id']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_parent_id']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetParentId", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a40e00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityGetParentId", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entity_id: str, type: Literal['entity_get_parent_id'] = 'entity_get_parent_id') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entity_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['entity_get_parent_id'], required=False, default='entity_get_parent_id')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEntityGetSketchPaths(**data)[source][source]
What are all UUIDs of all the paths sketched on top of this entity?
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entity_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_sketch_paths']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEntityGetSketchPaths'>, 'config': {'title': 'OptionEntityGetSketchPaths'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEntityGetSketchPaths'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEntityGetSketchPaths'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEntityGetSketchPaths:94467866912928', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_sketch_paths', 'schema': {'expected': ['entity_get_sketch_paths'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetSketchPaths', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a668a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4dcb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_sketch_paths", }, expected_py: None, name: "literal['entity_get_sketch_paths']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetSketchPaths", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetSketchPaths", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14aeaf530, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14aeafdb0, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b023090, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b022cd0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4dcb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_sketch_paths": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4dcb0, ), ], }, expected_repr: "'entity_get_sketch_paths'", name: "literal['entity_get_sketch_paths']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_sketch_paths']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetSketchPaths", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a668a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityGetSketchPaths", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entity_id: str, type: Literal['entity_get_sketch_paths'] = 'entity_get_sketch_paths') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entity_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['entity_get_sketch_paths'], required=False, default='entity_get_sketch_paths')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEntityLinearPattern(**data)[source][source]
Create a linear pattern using this entity.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'axis': <class 'kittycad.models.point3d.Point3d'>, 'entity_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'num_repetitions': <class 'int'>, 'spacing': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': typing.Literal['entity_linear_pattern']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEntityLinearPattern'>, 'config': {'title': 'OptionEntityLinearPattern'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEntityLinearPattern'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEntityLinearPattern'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEntityLinearPattern:94467866964880', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'num_repetitions': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}, 'spacing': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_linear_pattern', 'schema': {'expected': ['entity_linear_pattern'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityLinearPattern', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a73390, ), serializer: Fields( GeneralFieldsSerializer { fields: { "axis": SerField { key_py: Py( 0x00007fa14c13dce0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0330e750, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), ), required: true, }, "spacing": SerField { key_py: Py( 0x00007fa14e3a77b0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "num_repetitions": SerField { key_py: Py( 0x00007fa14aff6470, ), alias: None, alias_py: None, serializer: Some( Int( IntSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4ddb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_linear_pattern", }, expected_py: None, name: "literal['entity_linear_pattern']", }, ), }, ), ), required: true, }, "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "OptionEntityLinearPattern", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityLinearPattern", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "axis", lookup_key: Simple { key: "axis", py_key: Py( 0x00007fa14c528720, ), path: LookupPath( [ S( "axis", Py( 0x00007fa14b0605a0, ), ), ], ), }, name_py: Py( 0x00007fa14c13dce0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0330e750, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point3d", }, ), frozen: false, }, Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14ae385b0, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14af84bf0, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "num_repetitions", lookup_key: Simple { key: "num_repetitions", py_key: Py( 0x00007fa14ae34e70, ), path: LookupPath( [ S( "num_repetitions", Py( 0x00007fa14ae34b30, ), ), ], ), }, name_py: Py( 0x00007fa14aff6470, ), validator: Int( IntValidator { strict: false, }, ), frozen: false, }, Field { name: "spacing", lookup_key: Simple { key: "spacing", py_key: Py( 0x00007fa14b061740, ), path: LookupPath( [ S( "spacing", Py( 0x00007fa14b1159b0, ), ), ], ), }, name_py: Py( 0x00007fa14e3a77b0, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14aec6780, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b1147b0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b117c60, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4ddb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_linear_pattern": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4ddb0, ), ], }, expected_repr: "'entity_linear_pattern'", name: "literal['entity_linear_pattern']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_linear_pattern']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityLinearPattern", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a73390, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityLinearPattern", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, axis: kittycad.models.point3d.Point3d, entity_id: str, num_repetitions: int, spacing: kittycad.models.length_unit.LengthUnit, type: Literal['entity_linear_pattern'] = 'entity_linear_pattern') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'axis': FieldInfo(annotation=Point3d, required=True), 'entity_id': FieldInfo(annotation=str, required=True), 'num_repetitions': FieldInfo(annotation=int, required=True), 'spacing': FieldInfo(annotation=LengthUnit, required=True), 'type': FieldInfo(annotation=Literal['entity_linear_pattern'], required=False, default='entity_linear_pattern')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
spacing:
LengthUnit[source]
- class kittycad.models.modeling_cmd.OptionEntityLinearPatternTransform(**data)[source][source]
Create a pattern using this entity by specifying the transform for each desired repetition. Transformations are performed in the following order (first applied to last applied): scale, rotate, translate.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entity_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'transform': typing.List[kittycad.models.transform.Transform], 'type': typing.Literal['entity_linear_pattern_transform']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.modeling_cmd.OptionEntityLinearPatternTransform'>, 'config': {'title': 'OptionEntityLinearPatternTransform'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEntityLinearPatternTransform'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEntityLinearPatternTransform'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEntityLinearPatternTransform:94467866930128', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'transform': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'cls': <class 'kittycad.models.transform.Transform'>, 'config': {'title': 'Transform'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.transform.Transform'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.transform.Transform'>>]}, 'ref': 'kittycad.models.transform.Transform:94467866354592', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'replicate': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': True, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'rotation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': {'angle': {'unit': 'degrees', 'value': 0.0}, 'axis': {'x': 0.0, 'y': 0.0, 'z': 1.0}, 'origin': {'type': 'local'}}, 'schema': {'cls': <class 'kittycad.models.rotation.Rotation'>, 'config': {'title': 'Rotation'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.rotation.Rotation'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.rotation.Rotation'>>]}, 'ref': 'kittycad.models.rotation.Rotation:94467866320864', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'angle': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.angle.Angle'>, 'config': {'title': 'Angle'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.angle.Angle'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.angle.Angle'>>]}, 'ref': 'kittycad.models.angle.Angle:94467862185984', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitAngle'>, 'members': [UnitAngle.DEGREES, UnitAngle.RADIANS], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_angle.UnitAngle:94467859501968', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'value': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Angle', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'origin': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'pydantic.root_model.RootModel[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, 'config': {'title': "RootModel[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]"}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'pydantic.root_model.RootModel[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'pydantic.root_model.RootModel[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>>]}, 'ref': "pydantic.root_model.RootModel:94467864067568[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]:140330724571040]", 'root_model': True, 'schema': {'choices': {'custom': {'cls': <class 'kittycad.models.origin_type.OptionCustom'>, 'config': {'title': 'OptionCustom'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.origin_type.OptionCustom'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.origin_type.OptionCustom'>>]}, 'ref': 'kittycad.models.origin_type.OptionCustom:94467866280912', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'origin': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'custom', 'schema': {'expected': ['custom'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCustom', 'type': 'model-fields'}, 'type': 'model'}, 'global': {'cls': <class 'kittycad.models.origin_type.OptionGlobal'>, 'config': {'title': 'OptionGlobal'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.origin_type.OptionGlobal'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.origin_type.OptionGlobal'>>]}, 'ref': 'kittycad.models.origin_type.OptionGlobal:94467866274192', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'global', 'schema': {'expected': ['global'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionGlobal', 'type': 'model-fields'}, 'type': 'model'}, 'local': {'cls': <class 'kittycad.models.origin_type.OptionLocal'>, 'config': {'title': 'OptionLocal'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.origin_type.OptionLocal'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.origin_type.OptionLocal'>>]}, 'ref': 'kittycad.models.origin_type.OptionLocal:94467864572976', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'local', 'schema': {'expected': ['local'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionLocal', 'type': 'model-fields'}, 'type': 'model'}}, 'discriminator': 'type', 'from_attributes': True, 'metadata': {}, 'strict': False, 'type': 'tagged-union'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'Rotation', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'default'}, 'type': 'model-field'}, 'scale': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': {'x': 1.0, 'y': 1.0, 'z': 1.0}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'default'}, 'type': 'model-field'}, 'translate': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': {'x': 0.0, 'y': 0.0, 'z': 0.0}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'Transform', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_linear_pattern_transform', 'schema': {'expected': ['entity_linear_pattern_transform'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityLinearPatternTransform', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a6abd0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "transform": SerField { key_py: Py( 0x00007fa14e4f7db0, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Model( ModelSerializer { class: Py( 0x000055eb039de3a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "replicate": SerField { key_py: Py( 0x00007fa14d85ce30, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f930d00, ), ), serializer: Bool( BoolSerializer, ), }, ), ), required: true, }, "rotation": SerField { key_py: Py( 0x00007fa14d68d8b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14afd5580, ), ), serializer: Model( ModelSerializer { class: Py( 0x000055eb039d5fe0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "origin": SerField { key_py: Py( 0x00007fa14fa3aa98, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb039d04d0, ), serializer: TaggedUnion( TaggedUnionSerializer { discriminator: LookupKey( Simple { key: "type", py_key: Py( 0x00007fa14fa3c5a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14fa3c5a0, ), ), ], ), }, ), lookup: { "global": 1, "local": 0, "custom": 2, }, choices: [ Model( ModelSerializer { class: Py( 0x000055eb0382b430, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14e946dc0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "local", }, expected_py: None, name: "literal['local']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionLocal", }, ), Model( ModelSerializer { class: Py( 0x000055eb039ca990, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14eb89380, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "global", }, expected_py: None, name: "literal['global']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionGlobal", }, ), Model( ModelSerializer { class: Py( 0x000055eb039cc3d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "origin": SerField { key_py: Py( 0x00007fa14fa3aa98, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14afd39c0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "custom", }, expected_py: None, name: "literal['custom']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCustom", }, ), ], name: "TaggedUnion[OptionLocal, OptionGlobal, OptionCustom]", }, ), has_extra: false, root_model: true, name: "RootModel[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]", }, ), ), required: true, }, "angle": SerField { key_py: Py( 0x00007fa14d9ed140, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb035e4800, ), serializer: Fields( GeneralFieldsSerializer { fields: { "value": SerField { key_py: Py( 0x00007fa14fa3c768, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "unit": SerField { key_py: Py( 0x00007fa14e161380, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb03355390, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Angle", }, ), ), required: true, }, "axis": SerField { key_py: Py( 0x00007fa14c13dce0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Rotation", }, ), }, ), ), required: true, }, "scale": SerField { key_py: Py( 0x00007fa14e088d20, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14afe2440, ), ), serializer: Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), }, ), ), required: true, }, "translate": SerField { key_py: Py( 0x00007fa14fa3c4a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14afd6b00, ), ), serializer: Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Transform", }, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[Transform]", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2a1f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_linear_pattern_transform", }, expected_py: None, name: "literal['entity_linear_pattern_transform']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionEntityLinearPatternTransform", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityLinearPatternTransform", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14aec5270, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14aec5230, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "transform", lookup_key: Simple { key: "transform", py_key: Py( 0x00007fa14aec5430, ), path: LookupPath( [ S( "transform", Py( 0x00007fa14aec5470, ), ), ], ), }, name_py: Py( 0x00007fa14e4f7db0, ), validator: List( ListValidator { strict: false, item_validator: Some( Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "replicate", lookup_key: Simple { key: "replicate", py_key: Py( 0x00007fa14aec52f0, ), path: LookupPath( [ S( "replicate", Py( 0x00007fa14aec52b0, ), ), ], ), }, name_py: Py( 0x00007fa14d85ce30, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f930d00, ), ), on_error: Raise, validator: Bool( BoolValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[bool]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "rotation", lookup_key: Simple { key: "rotation", py_key: Py( 0x00007fa14aec5330, ), path: LookupPath( [ S( "rotation", Py( 0x00007fa14aec5370, ), ), ], ), }, name_py: Py( 0x00007fa14d68d8b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14afd5580, ), ), on_error: Raise, validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "angle", lookup_key: Simple { key: "angle", py_key: Py( 0x00007fa14b023930, ), path: LookupPath( [ S( "angle", Py( 0x00007fa14b023960, ), ), ], ), }, name_py: Py( 0x00007fa14d9ed140, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "unit", lookup_key: Simple { key: "unit", py_key: Py( 0x00007fa14b023870, ), path: LookupPath( [ S( "unit", Py( 0x00007fa14b0238a0, ), ), ], ), }, name_py: Py( 0x00007fa14e161380, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb03355390, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "degrees": 0, "radians": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b361850, ), Py( 0x00007fa14b3618b0, ), ], }, missing: None, expected_repr: "'degrees' or 'radians'", strict: false, class_repr: "UnitAngle", name: "str-enum[UnitAngle]", }, ), frozen: false, }, Field { name: "value", lookup_key: Simple { key: "value", py_key: Py( 0x00007fa14b0238d0, ), path: LookupPath( [ S( "value", Py( 0x00007fa14b023900, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c768, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Angle", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb035e4800, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Angle", }, ), frozen: false, }, Field { name: "axis", lookup_key: Simple { key: "axis", py_key: Py( 0x00007fa14b023990, ), path: LookupPath( [ S( "axis", Py( 0x00007fa14b0239c0, ), ), ], ), }, name_py: Py( 0x00007fa14c13dce0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "Point3d", }, ), frozen: false, }, Field { name: "origin", lookup_key: Simple { key: "origin", py_key: Py( 0x00007fa14b023b70, ), path: LookupPath( [ S( "origin", Py( 0x00007fa14b023ba0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3aa98, ), validator: Model( ModelValidator { revalidate: Never, validator: TaggedUnion( TaggedUnionValidator { discriminator: LookupKey( Simple { key: "type", py_key: Py( 0x00007fa14fa3c5a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14fa3c5a0, ), ), ], ), }, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "global": 1, "custom": 2, "local": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b0239f0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b023a20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14e946dc0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "local": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14e946dc0, ), ], }, expected_repr: "'local'", name: "literal['local']", }, ), validate_default: false, copy_default: false, name: "default[literal['local']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionLocal", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0382b430, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionLocal", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b023a50, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b023a80, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14eb89380, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "global": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14eb89380, ), ], }, expected_repr: "'global'", name: "literal['global']", }, ), validate_default: false, copy_default: false, name: "default[literal['global']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionGlobal", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039ca990, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionGlobal", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "origin", lookup_key: Simple { key: "origin", py_key: Py( 0x00007fa14b023ab0, ), path: LookupPath( [ S( "origin", Py( 0x00007fa14b023ae0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3aa98, ), validator: DefinitionRef( DefinitionRefValidator { definition: "Point3d", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b023b10, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b023b40, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14afd39c0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "custom": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14afd39c0, ), ], }, expected_repr: "'custom'", name: "literal['custom']", }, ), validate_default: false, copy_default: false, name: "default[literal['custom']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionCustom", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039cc3d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCustom", }, ), ], }, from_attributes: true, strict: false, custom_error: None, tags_repr: "'local', 'global', 'custom'", discriminator_repr: "'type'", name: "tagged-union[OptionLocal,OptionGlobal,OptionCustom]", }, ), class: Py( 0x000055eb039d04d0, ), post_init: None, frozen: false, custom_init: false, root_model: true, undefined: Py( 0x00007fa14d802350, ), name: "RootModel[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]", }, ), frozen: false, }, ], model_name: "Rotation", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039d5fe0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Rotation", }, ), validate_default: false, copy_default: true, name: "default[Rotation]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "scale", lookup_key: Simple { key: "scale", py_key: Py( 0x00007fa14b023bd0, ), path: LookupPath( [ S( "scale", Py( 0x00007fa14b023c00, ), ), ], ), }, name_py: Py( 0x00007fa14e088d20, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14afe2440, ), ), on_error: Raise, validator: DefinitionRef( DefinitionRefValidator { definition: "Point3d", }, ), validate_default: false, copy_default: true, name: "default[Point3d]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "translate", lookup_key: Simple { key: "translate", py_key: Py( 0x00007fa14aec53b0, ), path: LookupPath( [ S( "translate", Py( 0x00007fa14aec53f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c4a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14afd6b00, ), ), on_error: Raise, validator: DefinitionRef( DefinitionRefValidator { definition: "Point3d", }, ), validate_default: false, copy_default: true, name: "default[Point3d]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "Transform", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039de3a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Transform", }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b023c30, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b023c60, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2a1f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_linear_pattern_transform": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2a1f0, ), ], }, expected_repr: "'entity_linear_pattern_transform'", name: "literal['entity_linear_pattern_transform']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_linear_pattern_transform']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityLinearPatternTransform", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a6abd0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityLinearPatternTransform", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entity_id: str, transform: List[kittycad.models.transform.Transform], type: Literal['entity_linear_pattern_transform'] = 'entity_linear_pattern_transform') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entity_id': FieldInfo(annotation=str, required=True), 'transform': FieldInfo(annotation=List[Transform], required=True), 'type': FieldInfo(annotation=Literal['entity_linear_pattern_transform'], required=False, default='entity_linear_pattern_transform')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEntityMakeHelix(**data)[source][source]
Create a helix using the input cylinder and other specified parameters.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'cylinder_id': <class 'str'>, 'is_clockwise': <class 'bool'>, 'length': <class 'kittycad.models.length_unit.LengthUnit'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'revolutions': <class 'float'>, 'start_angle': <class 'kittycad.models.angle.Angle'>, 'type': typing.Literal['entity_make_helix']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEntityMakeHelix'>, 'config': {'title': 'OptionEntityMakeHelix'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEntityMakeHelix'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEntityMakeHelix'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEntityMakeHelix:94467866954016', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'cylinder_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'is_clockwise': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'length': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'revolutions': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'start_angle': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.angle.Angle'>, 'config': {'title': 'Angle'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.angle.Angle'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.angle.Angle'>>]}, 'ref': 'kittycad.models.angle.Angle:94467862185984', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitAngle'>, 'members': [UnitAngle.DEGREES, UnitAngle.RADIANS], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_angle.UnitAngle:94467859501968', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'value': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Angle', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_make_helix', 'schema': {'expected': ['entity_make_helix'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityMakeHelix', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a70920, ), serializer: Fields( GeneralFieldsSerializer { fields: { "length": SerField { key_py: Py( 0x00007fa14fa39ba8, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "start_angle": SerField { key_py: Py( 0x00007fa14aff69b0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb035e4800, ), serializer: Fields( GeneralFieldsSerializer { fields: { "value": SerField { key_py: Py( 0x00007fa14fa3c768, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "unit": SerField { key_py: Py( 0x00007fa14e161380, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb03355390, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Angle", }, ), ), required: true, }, "cylinder_id": SerField { key_py: Py( 0x00007fa14aff68b0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "is_clockwise": SerField { key_py: Py( 0x00007fa14aff68f0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "revolutions": SerField { key_py: Py( 0x00007fa14aff6930, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4df30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_make_helix", }, expected_py: None, name: "literal['entity_make_helix']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 6, }, ), has_extra: false, root_model: false, name: "OptionEntityMakeHelix", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityMakeHelix", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "cylinder_id", lookup_key: Simple { key: "cylinder_id", py_key: Py( 0x00007fa14c2f2f30, ), path: LookupPath( [ S( "cylinder_id", Py( 0x00007fa14c2f33b0, ), ), ], ), }, name_py: Py( 0x00007fa14aff68b0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "is_clockwise", lookup_key: Simple { key: "is_clockwise", py_key: Py( 0x00007fa14c2f2e70, ), path: LookupPath( [ S( "is_clockwise", Py( 0x00007fa14c2f30f0, ), ), ], ), }, name_py: Py( 0x00007fa14aff68f0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "length", lookup_key: Simple { key: "length", py_key: Py( 0x00007fa14afe5680, ), path: LookupPath( [ S( "length", Py( 0x00007fa14afe58f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa39ba8, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14c2f1c00, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "revolutions", lookup_key: Simple { key: "revolutions", py_key: Py( 0x00007fa14c2f2cf0, ), path: LookupPath( [ S( "revolutions", Py( 0x00007fa14c2f3170, ), ), ], ), }, name_py: Py( 0x00007fa14aff6930, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "start_angle", lookup_key: Simple { key: "start_angle", py_key: Py( 0x00007fa14c2f3370, ), path: LookupPath( [ S( "start_angle", Py( 0x00007fa14c2f1530, ), ), ], ), }, name_py: Py( 0x00007fa14aff69b0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "unit", lookup_key: Simple { key: "unit", py_key: Py( 0x00007fa14afe6100, ), path: LookupPath( [ S( "unit", Py( 0x00007fa14afe6a00, ), ), ], ), }, name_py: Py( 0x00007fa14e161380, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb03355390, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "degrees": 0, "radians": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b361850, ), Py( 0x00007fa14b3618b0, ), ], }, missing: None, expected_repr: "'degrees' or 'radians'", strict: false, class_repr: "UnitAngle", name: "str-enum[UnitAngle]", }, ), frozen: false, }, Field { name: "value", lookup_key: Simple { key: "value", py_key: Py( 0x00007fa14afe5cb0, ), path: LookupPath( [ S( "value", Py( 0x00007fa14afe70f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c768, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Angle", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb035e4800, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Angle", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe6670, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe6580, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4df30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_make_helix": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4df30, ), ], }, expected_repr: "'entity_make_helix'", name: "literal['entity_make_helix']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_make_helix']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityMakeHelix", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a70920, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityMakeHelix", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, cylinder_id: str, is_clockwise: bool, length: kittycad.models.length_unit.LengthUnit, revolutions: float, start_angle: kittycad.models.angle.Angle, type: Literal['entity_make_helix'] = 'entity_make_helix') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
-
length:
LengthUnit[source]
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'cylinder_id': FieldInfo(annotation=str, required=True), 'is_clockwise': FieldInfo(annotation=bool, required=True), 'length': FieldInfo(annotation=LengthUnit, required=True), 'revolutions': FieldInfo(annotation=float, required=True), 'start_angle': FieldInfo(annotation=Angle, required=True), 'type': FieldInfo(annotation=Literal['entity_make_helix'], required=False, default='entity_make_helix')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEntityMirror(**data)[source][source]
Mirror the input entities over the specified axis. (Currently only supports sketches)
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'axis': <class 'kittycad.models.point3d.Point3d'>, 'ids': typing.List[str], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'point': <class 'kittycad.models.point3d.Point3d'>, 'type': typing.Literal['entity_mirror']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.modeling_cmd.OptionEntityMirror'>, 'config': {'title': 'OptionEntityMirror'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEntityMirror'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEntityMirror'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEntityMirror:94467867042688', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'point': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_mirror', 'schema': {'expected': ['entity_mirror'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityMirror', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a86380, ), serializer: Fields( GeneralFieldsSerializer { fields: { "ids": SerField { key_py: Py( 0x00007fa14e9d8c60, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "point": SerField { key_py: Py( 0x00007fa14d77f600, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4dfb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_mirror", }, expected_py: None, name: "literal['entity_mirror']", }, ), }, ), ), required: true, }, "axis": SerField { key_py: Py( 0x00007fa14c13dce0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "OptionEntityMirror", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityMirror", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "axis", lookup_key: Simple { key: "axis", py_key: Py( 0x00007fa14c12bba0, ), path: LookupPath( [ S( "axis", Py( 0x00007fa14c12bb70, ), ), ], ), }, name_py: Py( 0x00007fa14c13dce0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "ids", lookup_key: Simple { key: "ids", py_key: Py( 0x00007fa14c12bb40, ), path: LookupPath( [ S( "ids", Py( 0x00007fa14b024210, ), ), ], ), }, name_py: Py( 0x00007fa14e9d8c60, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "point", lookup_key: Simple { key: "point", py_key: Py( 0x00007fa14b0241e0, ), path: LookupPath( [ S( "point", Py( 0x00007fa14b024270, ), ), ], ), }, name_py: Py( 0x00007fa14d77f600, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b0240c0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b024030, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4dfb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_mirror": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4dfb0, ), ], }, expected_repr: "'entity_mirror'", name: "literal['entity_mirror']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_mirror']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityMirror", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a86380, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityMirror", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, axis: kittycad.models.point3d.Point3d, ids: List[str], point: kittycad.models.point3d.Point3d, type: Literal['entity_mirror'] = 'entity_mirror') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'axis': FieldInfo(annotation=Point3d, required=True), 'ids': FieldInfo(annotation=List[str], required=True), 'point': FieldInfo(annotation=Point3d, required=True), 'type': FieldInfo(annotation=Literal['entity_mirror'], required=False, default='entity_mirror')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEntityMirrorAcrossEdge(**data)[source][source]
Mirror the input entities over the specified edge. (Currently only supports sketches)
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'edge_id': <class 'str'>, 'ids': typing.List[str], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_mirror_across_edge']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEntityMirrorAcrossEdge'>, 'config': {'title': 'OptionEntityMirrorAcrossEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEntityMirrorAcrossEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEntityMirrorAcrossEdge'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEntityMirrorAcrossEdge:94467850108288', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edge_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_mirror_across_edge', 'schema': {'expected': ['entity_mirror_across_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityMirrorAcrossEdge', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb02a5fd80, ), serializer: Fields( GeneralFieldsSerializer { fields: { "edge_id": SerField { key_py: Py( 0x00007fa14afd2a60, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "ids": SerField { key_py: Py( 0x00007fa14e9d8c60, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2a150, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_mirror_across_edge", }, expected_py: None, name: "literal['entity_mirror_across_edge']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionEntityMirrorAcrossEdge", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityMirrorAcrossEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edge_id", lookup_key: Simple { key: "edge_id", py_key: Py( 0x00007fa14b0234e0, ), path: LookupPath( [ S( "edge_id", Py( 0x00007fa14b023db0, ), ), ], ), }, name_py: Py( 0x00007fa14afd2a60, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "ids", lookup_key: Simple { key: "ids", py_key: Py( 0x00007fa14b023f60, ), path: LookupPath( [ S( "ids", Py( 0x00007fa14b022760, ), ), ], ), }, name_py: Py( 0x00007fa14e9d8c60, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b0224f0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b0218f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2a150, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_mirror_across_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2a150, ), ], }, expected_repr: "'entity_mirror_across_edge'", name: "literal['entity_mirror_across_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_mirror_across_edge']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityMirrorAcrossEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb02a5fd80, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityMirrorAcrossEdge", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, edge_id: str, ids: List[str], type: Literal['entity_mirror_across_edge'] = 'entity_mirror_across_edge') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'edge_id': FieldInfo(annotation=str, required=True), 'ids': FieldInfo(annotation=List[str], required=True), 'type': FieldInfo(annotation=Literal['entity_mirror_across_edge'], required=False, default='entity_mirror_across_edge')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionEntitySetOpacity(**data)[source][source]
Set opacity of the entity.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entity_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'opacity': <class 'float'>, 'type': typing.Literal['entity_set_opacity']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionEntitySetOpacity'>, 'config': {'title': 'OptionEntitySetOpacity'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionEntitySetOpacity'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionEntitySetOpacity'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionEntitySetOpacity:94467867573008', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'opacity': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_set_opacity', 'schema': {'expected': ['entity_set_opacity'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntitySetOpacity', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b07b10, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4e130, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_set_opacity", }, expected_py: None, name: "literal['entity_set_opacity']", }, ), }, ), ), required: true, }, "opacity": SerField { key_py: Py( 0x00007fa14e088870, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionEntitySetOpacity", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntitySetOpacity", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14aebb370, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14ad3ebb0, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "opacity", lookup_key: Simple { key: "opacity", py_key: Py( 0x00007fa14ae76ac0, ), path: LookupPath( [ S( "opacity", Py( 0x00007fa14ae76a60, ), ), ], ), }, name_py: Py( 0x00007fa14e088870, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae745d0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae745a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4e130, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_set_opacity": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4e130, ), ], }, expected_repr: "'entity_set_opacity'", name: "literal['entity_set_opacity']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_set_opacity']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntitySetOpacity", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b07b10, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntitySetOpacity", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entity_id: str, opacity: float, type: Literal['entity_set_opacity'] = 'entity_set_opacity') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entity_id': FieldInfo(annotation=str, required=True), 'opacity': FieldInfo(annotation=float, required=True), 'type': FieldInfo(annotation=Literal['entity_set_opacity'], required=False, default='entity_set_opacity')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionExport(**data)[source][source]
Export the scene to a file.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entity_ids': typing.List[str], 'format': <class 'pydantic.root_model.RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['export']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>, 'config': {'title': 'AxisDirectionPair'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>>]}, 'ref': 'kittycad.models.axis_direction_pair.AxisDirectionPair:94467855533120', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.axis.Axis:94467855528000', 'type': 'definition-ref'}, 'type': 'model-field'}, 'direction': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.direction.Direction:94467855530560', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'AxisDirectionPair', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <enum 'Axis'>, 'members': [Axis.Y, Axis.Z], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.axis.Axis:94467855528000', 'sub_type': 'str', 'type': 'enum'}, {'cls': <enum 'Direction'>, 'members': [Direction.POSITIVE, Direction.NEGATIVE], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.direction.Direction:94467855530560', 'sub_type': 'str', 'type': 'enum'}, {'cls': <class 'kittycad.models.system.System'>, 'config': {'title': 'System'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.system.System'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.system.System'>>]}, 'ref': 'kittycad.models.system.System:94467861035232', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'forward': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.axis_direction_pair.AxisDirectionPair:94467855533120', 'type': 'definition-ref'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.axis_direction_pair.AxisDirectionPair:94467855533120', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'System', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <enum 'UnitLength'>, 'members': [UnitLength.CM, UnitLength.FT, UnitLength.IN, UnitLength.M, UnitLength.MM, UnitLength.YD], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_length.UnitLength:94467861018320', 'sub_type': 'str', 'type': 'enum'}, {'cls': <class 'kittycad.models.selection.OptionDefaultScene'>, 'config': {'title': 'OptionDefaultScene'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.selection.OptionDefaultScene'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.selection.OptionDefaultScene'>>]}, 'ref': 'kittycad.models.selection.OptionDefaultScene:94467862099456', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_scene', 'schema': {'expected': ['default_scene'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultScene', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <class 'kittycad.models.selection.OptionSceneByIndex'>, 'config': {'title': 'OptionSceneByIndex'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.selection.OptionSceneByIndex'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.selection.OptionSceneByIndex'>>]}, 'ref': 'kittycad.models.selection.OptionSceneByIndex:94467862123456', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'index': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'scene_by_index', 'schema': {'expected': ['scene_by_index'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSceneByIndex', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <class 'kittycad.models.selection.OptionSceneByName'>, 'config': {'title': 'OptionSceneByName'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.selection.OptionSceneByName'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.selection.OptionSceneByName'>>]}, 'ref': 'kittycad.models.selection.OptionSceneByName:94467862131408', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'scene_by_name', 'schema': {'expected': ['scene_by_name'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSceneByName', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <class 'kittycad.models.selection.OptionMeshByIndex'>, 'config': {'title': 'OptionMeshByIndex'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.selection.OptionMeshByIndex'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.selection.OptionMeshByIndex'>>]}, 'ref': 'kittycad.models.selection.OptionMeshByIndex:94467862113408', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'index': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'mesh_by_index', 'schema': {'expected': ['mesh_by_index'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMeshByIndex', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <class 'kittycad.models.selection.OptionMeshByName'>, 'config': {'title': 'OptionMeshByName'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.selection.OptionMeshByName'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.selection.OptionMeshByName'>>]}, 'ref': 'kittycad.models.selection.OptionMeshByName:94467861678960', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'mesh_by_name', 'schema': {'expected': ['mesh_by_name'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMeshByName', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <class 'pydantic.root_model.RootModel[Annotated[Union[OptionDefaultScene, OptionSceneByIndex, OptionSceneByName, OptionMeshByIndex, OptionMeshByName], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, 'config': {'title': "RootModel[Annotated[Union[OptionDefaultScene, OptionSceneByIndex, OptionSceneByName, OptionMeshByIndex, OptionMeshByName], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]"}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'pydantic.root_model.RootModel[Annotated[Union[OptionDefaultScene, OptionSceneByIndex, OptionSceneByName, OptionMeshByIndex, OptionMeshByName], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'pydantic.root_model.RootModel[Annotated[Union[OptionDefaultScene, OptionSceneByIndex, OptionSceneByName, OptionMeshByIndex, OptionMeshByName], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>>]}, 'ref': "pydantic.root_model.RootModel:94467864067568[Annotated[Union[OptionDefaultScene, OptionSceneByIndex, OptionSceneByName, OptionMeshByIndex, OptionMeshByName], FieldInfo(annotation=NoneType, required=True, discriminator='type')]:140330737852128]", 'root_model': True, 'schema': {'choices': {'default_scene': {'schema_ref': 'kittycad.models.selection.OptionDefaultScene:94467862099456', 'type': 'definition-ref'}, 'mesh_by_index': {'schema_ref': 'kittycad.models.selection.OptionMeshByIndex:94467862113408', 'type': 'definition-ref'}, 'mesh_by_name': {'schema_ref': 'kittycad.models.selection.OptionMeshByName:94467861678960', 'type': 'definition-ref'}, 'scene_by_index': {'schema_ref': 'kittycad.models.selection.OptionSceneByIndex:94467862123456', 'type': 'definition-ref'}, 'scene_by_name': {'schema_ref': 'kittycad.models.selection.OptionSceneByName:94467862131408', 'type': 'definition-ref'}}, 'discriminator': 'type', 'from_attributes': True, 'metadata': {}, 'strict': False, 'type': 'tagged-union'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.modeling_cmd.OptionExport'>, 'config': {'title': 'OptionExport'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionExport'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionExport'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionExport:94467866750320', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'format': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'pydantic.root_model.RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, 'config': {'title': "RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]"}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'pydantic.root_model.RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'pydantic.root_model.RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>>]}, 'ref': "pydantic.root_model.RootModel:94467864067568[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]:140330738840208]", 'root_model': True, 'schema': {'choices': {'fbx': {'cls': <class 'kittycad.models.output_format.OptionFbx'>, 'config': {'title': 'OptionFbx'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.output_format.OptionFbx'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.output_format.OptionFbx'>>]}, 'ref': 'kittycad.models.output_format.OptionFbx:94467861728544', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'storage': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'FbxStorage'>, 'members': [FbxStorage.ASCII, FbxStorage.BINARY], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.fbx_storage.FbxStorage:94467862089392', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'fbx', 'schema': {'expected': ['fbx'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionFbx', 'type': 'model-fields'}, 'type': 'model'}, 'gltf': {'cls': <class 'kittycad.models.output_format.OptionGltf'>, 'config': {'title': 'OptionGltf'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.output_format.OptionGltf'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.output_format.OptionGltf'>>]}, 'ref': 'kittycad.models.output_format.OptionGltf:94467861737840', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'presentation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'GltfPresentation'>, 'members': [GltfPresentation.COMPACT, GltfPresentation.PRETTY], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.gltf_presentation.GltfPresentation:94467862092560', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'storage': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'GltfStorage'>, 'members': [GltfStorage.BINARY, GltfStorage.STANDARD, GltfStorage.EMBEDDED], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.gltf_storage.GltfStorage:94467862094336', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'gltf', 'schema': {'expected': ['gltf'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionGltf', 'type': 'model-fields'}, 'type': 'model'}, 'obj': {'cls': <class 'kittycad.models.output_format.OptionObj'>, 'config': {'title': 'OptionObj'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.output_format.OptionObj'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.output_format.OptionObj'>>]}, 'ref': 'kittycad.models.output_format.OptionObj:94467861753744', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'coords': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.system.System:94467861035232', 'type': 'definition-ref'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'obj', 'schema': {'expected': ['obj'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'units': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.unit_length.UnitLength:94467861018320', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'OptionObj', 'type': 'model-fields'}, 'type': 'model'}, 'ply': {'cls': <class 'kittycad.models.output_format.OptionPly'>, 'config': {'title': 'OptionPly'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.output_format.OptionPly'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.output_format.OptionPly'>>]}, 'ref': 'kittycad.models.output_format.OptionPly:94467855184864', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'coords': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.system.System:94467861035232', 'type': 'definition-ref'}, 'type': 'model-field'}, 'selection': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': "pydantic.root_model.RootModel:94467864067568[Annotated[Union[OptionDefaultScene, OptionSceneByIndex, OptionSceneByName, OptionMeshByIndex, OptionMeshByName], FieldInfo(annotation=NoneType, required=True, discriminator='type')]:140330737852128]", 'type': 'definition-ref'}, 'type': 'model-field'}, 'storage': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'PlyStorage'>, 'members': [PlyStorage.ASCII, PlyStorage.BINARY_LITTLE_ENDIAN, PlyStorage.BINARY_BIG_ENDIAN], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.ply_storage.PlyStorage:94467862096896', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'ply', 'schema': {'expected': ['ply'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'units': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.unit_length.UnitLength:94467861018320', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'OptionPly', 'type': 'model-fields'}, 'type': 'model'}, 'step': {'cls': <class 'kittycad.models.output_format.OptionStep'>, 'config': {'title': 'OptionStep'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.output_format.OptionStep'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.output_format.OptionStep'>>]}, 'ref': 'kittycad.models.output_format.OptionStep:94467855246992', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'coords': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.system.System:94467861035232', 'type': 'definition-ref'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'step', 'schema': {'expected': ['step'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionStep', 'type': 'model-fields'}, 'type': 'model'}, 'stl': {'cls': <class 'kittycad.models.output_format.OptionStl'>, 'config': {'title': 'OptionStl'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.output_format.OptionStl'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.output_format.OptionStl'>>]}, 'ref': 'kittycad.models.output_format.OptionStl:94467855267488', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'coords': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.system.System:94467861035232', 'type': 'definition-ref'}, 'type': 'model-field'}, 'selection': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': "pydantic.root_model.RootModel:94467864067568[Annotated[Union[OptionDefaultScene, OptionSceneByIndex, OptionSceneByName, OptionMeshByIndex, OptionMeshByName], FieldInfo(annotation=NoneType, required=True, discriminator='type')]:140330737852128]", 'type': 'definition-ref'}, 'type': 'model-field'}, 'storage': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'StlStorage'>, 'members': [StlStorage.ASCII, StlStorage.BINARY], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.stl_storage.StlStorage:94467861712896', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'stl', 'schema': {'expected': ['stl'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'units': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.unit_length.UnitLength:94467861018320', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'OptionStl', 'type': 'model-fields'}, 'type': 'model'}}, 'discriminator': 'type', 'from_attributes': True, 'metadata': {}, 'strict': False, 'type': 'tagged-union'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'export', 'schema': {'expected': ['export'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionExport', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a3ed70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007fa14b038970, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14e164660, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "export", }, expected_py: None, name: "literal['export']", }, ), }, ), ), required: true, }, "format": SerField { key_py: Py( 0x00007fa14fa38b50, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0365a4c0, ), serializer: TaggedUnion( TaggedUnionSerializer { discriminator: LookupKey( Simple { key: "type", py_key: Py( 0x00007fa14fa3c5a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14fa3c5a0, ), ), ], ), }, ), lookup: { "step": 4, "fbx": 0, "obj": 2, "stl": 5, "ply": 3, "gltf": 1, }, choices: [ Model( ModelSerializer { class: Py( 0x000055eb03574d20, ), serializer: Fields( GeneralFieldsSerializer { fields: { "storage": SerField { key_py: Py( 0x00007fa14d76d020, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb035cceb0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd71170, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "fbx", }, expected_py: None, name: "literal['fbx']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionFbx", }, ), Model( ModelSerializer { class: Py( 0x000055eb03577170, ), serializer: Fields( GeneralFieldsSerializer { fields: { "storage": SerField { key_py: Py( 0x00007fa14d76d020, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb035ce200, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd710e0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "gltf", }, expected_py: None, name: "literal['gltf']", }, ), }, ), ), required: true, }, "presentation": SerField { key_py: Py( 0x00007fa14bb506b0, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb035cdb10, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionGltf", }, ), Model( ModelSerializer { class: Py( 0x000055eb0357af90, ), serializer: Fields( GeneralFieldsSerializer { fields: { "coords": SerField { key_py: Py( 0x00007fa14bd723a0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14fa3a760, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "obj", }, expected_py: None, name: "literal['obj']", }, ), }, ), ), required: true, }, "units": SerField { key_py: Py( 0x00007fa14e6aeac0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: false, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionObj", }, ), Model( ModelSerializer { class: Py( 0x000055eb02f373e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd71b00, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "ply", }, expected_py: None, name: "literal['ply']", }, ), }, ), ), required: true, }, "units": SerField { key_py: Py( 0x00007fa14e6aeac0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: false, }, ), ), required: true, }, "storage": SerField { key_py: Py( 0x00007fa14d76d020, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb035cec00, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "selection": SerField { key_py: Py( 0x00007fa14bd7bbf0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "coords": SerField { key_py: Py( 0x00007fa14bd723a0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "OptionPly", }, ), Model( ModelSerializer { class: Py( 0x000055eb02f46690, ), serializer: Fields( GeneralFieldsSerializer { fields: { "coords": SerField { key_py: Py( 0x00007fa14bd723a0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14fa3be10, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "step", }, expected_py: None, name: "literal['step']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionStep", }, ), Model( ModelSerializer { class: Py( 0x000055eb02f4b6a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "selection": SerField { key_py: Py( 0x00007fa14bd7bbf0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "coords": SerField { key_py: Py( 0x00007fa14bd723a0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "storage": SerField { key_py: Py( 0x00007fa14d76d020, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb03571000, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "units": SerField { key_py: Py( 0x00007fa14e6aeac0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: false, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd71ce0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "stl", }, expected_py: None, name: "literal['stl']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "OptionStl", }, ), ], name: "TaggedUnion[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionStep, OptionStl]", }, ), has_extra: false, root_model: true, name: "RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionExport", }, ), definitions=[Enum(EnumSerializer { class: Py(0x55eb02f8b040), serializer: Some(Str(StrSerializer)) }), Model(ModelSerializer { class: Py(0x55eb035cf600), serializer: Fields(GeneralFieldsSerializer { fields: {"type": SerField { key_py: Py(0x7fa14fa3c5a0), alias: None, alias_py: None, serializer: Some(WithDefault(WithDefaultSerializer { default: Default(Py(0x7fa14bb4ecf0)), serializer: Literal(LiteralSerializer { expected_int: {}, expected_str: {"default_scene"}, expected_py: None, name: "literal['default_scene']" }) })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 1 }), has_extra: false, root_model: false, name: "OptionDefaultScene" }), Model(ModelSerializer { class: Py(0x55eb02f8c440), serializer: Fields(GeneralFieldsSerializer { fields: {"direction": SerField { key_py: Py(0x7fa14e41ef30), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: false })), required: true }, "axis": SerField { key_py: Py(0x7fa14c13dce0), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: false })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 2 }), has_extra: false, root_model: false, name: "AxisDirectionPair" }), Model(ModelSerializer { class: Py(0x55eb034cb8e0), serializer: Fields(GeneralFieldsSerializer { fields: {"up": SerField { key_py: Py(0x7fa14d9ed0b0), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: true })), required: true }, "forward": SerField { key_py: Py(0x7fa14bd71cb0), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: true })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 2 }), has_extra: false, root_model: false, name: "System" }), Enum(EnumSerializer { class: Py(0x55eb034c76d0), serializer: Some(Str(StrSerializer)) }), Model(ModelSerializer { class: Py(0x55eb035d72d0), serializer: Fields(GeneralFieldsSerializer { fields: {"name": SerField { key_py: Py(0x7fa14fa3a388), alias: None, alias_py: None, serializer: Some(Str(StrSerializer)), required: true }, "type": SerField { key_py: Py(0x7fa14fa3c5a0), alias: None, alias_py: None, serializer: Some(WithDefault(WithDefaultSerializer { default: Default(Py(0x7fa14bb4cd70)), serializer: Literal(LiteralSerializer { expected_int: {}, expected_str: {"scene_by_name"}, expected_py: None, name: "literal['scene_by_name']" }) })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 2 }), has_extra: false, root_model: false, name: "OptionSceneByName" }), Model(ModelSerializer { class: Py(0x55eb035d2c80), serializer: Fields(GeneralFieldsSerializer { fields: {"type": SerField { key_py: Py(0x7fa14fa3c5a0), alias: None, alias_py: None, serializer: Some(WithDefault(WithDefaultSerializer { default: Default(Py(0x7fa14bb51570)), serializer: Literal(LiteralSerializer { expected_int: {}, expected_str: {"mesh_by_index"}, expected_py: None, name: "literal['mesh_by_index']" }) })), required: true }, "index": SerField { key_py: Py(0x7fa14f9faac0), alias: None, alias_py: None, serializer: Some(Int(IntSerializer)), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 2 }), has_extra: false, root_model: false, name: "OptionMeshByIndex" }), Model(ModelSerializer { class: Py(0x55eb033f6d30), serializer: TaggedUnion(TaggedUnionSerializer { discriminator: LookupKey(Simple { key: "type", py_key: Py(0x7fa14fa3c5a0), path: LookupPath([S("type", Py(0x7fa14fa3c5a0))]) }), lookup: {"default_scene": 0, "scene_by_name": 2, "scene_by_index": 1, "mesh_by_index": 3, "mesh_by_name": 4}, choices: [Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: true }), Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: true }), Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: true }), Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: true }), Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: true })], name: "TaggedUnion[definition-ref, definition-ref, definition-ref, definition-ref, definition-ref]" }), has_extra: false, root_model: true, name: "RootModel[Annotated[Union[OptionDefaultScene, OptionSceneByIndex, OptionSceneByName, OptionMeshByIndex, OptionMeshByName], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]" }), Model(ModelSerializer { class: Py(0x55eb035d53c0), serializer: Fields(GeneralFieldsSerializer { fields: {"type": SerField { key_py: Py(0x7fa14fa3c5a0), alias: None, alias_py: None, serializer: Some(WithDefault(WithDefaultSerializer { default: Default(Py(0x7fa14bb4caf0)), serializer: Literal(LiteralSerializer { expected_int: {}, expected_str: {"scene_by_index"}, expected_py: None, name: "literal['scene_by_index']" }) })), required: true }, "index": SerField { key_py: Py(0x7fa14f9faac0), alias: None, alias_py: None, serializer: Some(Int(IntSerializer)), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 2 }), has_extra: false, root_model: false, name: "OptionSceneByIndex" }), Enum(EnumSerializer { class: Py(0x55eb02f8ba40), serializer: Some(Str(StrSerializer)) }), Model(ModelSerializer { class: Py(0x55eb03568b70), serializer: Fields(GeneralFieldsSerializer { fields: {"name": SerField { key_py: Py(0x7fa14fa3a388), alias: None, alias_py: None, serializer: Some(Str(StrSerializer)), required: true }, "type": SerField { key_py: Py(0x7fa14fa3c5a0), alias: None, alias_py: None, serializer: Some(WithDefault(WithDefaultSerializer { default: Default(Py(0x7fa14bb516f0)), serializer: Literal(LiteralSerializer { expected_int: {}, expected_str: {"mesh_by_name"}, expected_py: None, name: "literal['mesh_by_name']" }) })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 2 }), has_extra: false, root_model: false, name: "OptionMeshByName" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionExport", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007fa14b09ae70, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007fa14b100730, ), ), ], ), }, name_py: Py( 0x00007fa14b038970, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "format", lookup_key: Simple { key: "format", py_key: Py( 0x00007fa14b021d40, ), path: LookupPath( [ S( "format", Py( 0x00007fa14b021c50, ), ), ], ), }, name_py: Py( 0x00007fa14fa38b50, ), validator: Model( ModelValidator { revalidate: Never, validator: TaggedUnion( TaggedUnionValidator { discriminator: LookupKey( Simple { key: "type", py_key: Py( 0x00007fa14fa3c5a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14fa3c5a0, ), ), ], ), }, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "ply": 3, "obj": 2, "fbx": 0, "gltf": 1, "step": 4, "stl": 5, }, ), expected_py_dict: None, expected_py_values: None, values: [ Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "storage", lookup_key: Simple { key: "storage", py_key: Py( 0x00007fa14b0206f0, ), path: LookupPath( [ S( "storage", Py( 0x00007fa14b0205d0, ), ), ], ), }, name_py: Py( 0x00007fa14d76d020, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb035cceb0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "binary": 1, "ascii": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c5f10, ), Py( 0x00007fa14b3c6090, ), ], }, missing: None, expected_repr: "'ascii' or 'binary'", strict: false, class_repr: "FbxStorage", name: "str-enum[FbxStorage]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b020b70, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b020990, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd71170, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "fbx": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd71170, ), ], }, expected_repr: "'fbx'", name: "literal['fbx']", }, ), validate_default: false, copy_default: false, name: "default[literal['fbx']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionFbx", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03574d20, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionFbx", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "presentation", lookup_key: Simple { key: "presentation", py_key: Py( 0x00007fa14ae3b2f0, ), path: LookupPath( [ S( "presentation", Py( 0x00007fa14ae38730, ), ), ], ), }, name_py: Py( 0x00007fa14bb506b0, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb035cdb10, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "compact": 0, "pretty": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c6150, ), Py( 0x00007fa14b3c6210, ), ], }, missing: None, expected_repr: "'compact' or 'pretty'", strict: false, class_repr: "GltfPresentation", name: "str-enum[GltfPresentation]", }, ), frozen: false, }, Field { name: "storage", lookup_key: Simple { key: "storage", py_key: Py( 0x00007fa14b0208a0, ), path: LookupPath( [ S( "storage", Py( 0x00007fa14b0208d0, ), ), ], ), }, name_py: Py( 0x00007fa14d76d020, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb035ce200, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "embedded": 2, "binary": 0, "standard": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c62d0, ), Py( 0x00007fa14b3c6390, ), Py( 0x00007fa14b3c6450, ), ], }, missing: None, expected_repr: "'binary', 'standard' or 'embedded'", strict: false, class_repr: "GltfStorage", name: "str-enum[GltfStorage]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b0206c0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b0205a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd710e0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "gltf": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd710e0, ), ], }, expected_repr: "'gltf'", name: "literal['gltf']", }, ), validate_default: false, copy_default: false, name: "default[literal['gltf']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionGltf", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03577170, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionGltf", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "coords", lookup_key: Simple { key: "coords", py_key: Py( 0x00007fa14b020a20, ), path: LookupPath( [ S( "coords", Py( 0x00007fa14b020a50, ), ), ], ), }, name_py: Py( 0x00007fa14bd723a0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b020a80, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b021bf0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14fa3a760, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "obj": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14fa3a760, ), ], }, expected_repr: "'obj'", name: "literal['obj']", }, ), validate_default: false, copy_default: false, name: "default[literal['obj']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "units", lookup_key: Simple { key: "units", py_key: Py( 0x00007fa14b0217d0, ), path: LookupPath( [ S( "units", Py( 0x00007fa14b0217a0, ), ), ], ), }, name_py: Py( 0x00007fa14e6aeac0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "OptionObj", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0357af90, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionObj", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "coords", lookup_key: Simple { key: "coords", py_key: Py( 0x00007fa14b0218c0, ), path: LookupPath( [ S( "coords", Py( 0x00007fa14b021980, ), ), ], ), }, name_py: Py( 0x00007fa14bd723a0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "selection", lookup_key: Simple { key: "selection", py_key: Py( 0x00007fa14ae2d830, ), path: LookupPath( [ S( "selection", Py( 0x00007fa14ae73230, ), ), ], ), }, name_py: Py( 0x00007fa14bd7bbf0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "storage", lookup_key: Simple { key: "storage", py_key: Py( 0x00007fa14b021920, ), path: LookupPath( [ S( "storage", Py( 0x00007fa14b021a70, ), ), ], ), }, name_py: Py( 0x00007fa14d76d020, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb035cec00, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "ascii": 0, "binary_little_endian": 1, "binary_big_endian": 2, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c64b0, ), Py( 0x00007fa14b3c6510, ), Py( 0x00007fa14b3c6570, ), ], }, missing: None, expected_repr: "'ascii', 'binary_little_endian' or 'binary_big_endian'", strict: false, class_repr: "PlyStorage", name: "str-enum[PlyStorage]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b021a40, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b0219b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd71b00, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "ply": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd71b00, ), ], }, expected_repr: "'ply'", name: "literal['ply']", }, ), validate_default: false, copy_default: false, name: "default[literal['ply']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "units", lookup_key: Simple { key: "units", py_key: Py( 0x00007fa14b021950, ), path: LookupPath( [ S( "units", Py( 0x00007fa14b0219e0, ), ), ], ), }, name_py: Py( 0x00007fa14e6aeac0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "OptionPly", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb02f373e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPly", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "coords", lookup_key: Simple { key: "coords", py_key: Py( 0x00007fa14b021a10, ), path: LookupPath( [ S( "coords", Py( 0x00007fa14b021b00, ), ), ], ), }, name_py: Py( 0x00007fa14bd723a0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b021aa0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b021c20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14fa3be10, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "step": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14fa3be10, ), ], }, expected_repr: "'step'", name: "literal['step']", }, ), validate_default: false, copy_default: false, name: "default[literal['step']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionStep", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb02f46690, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionStep", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "coords", lookup_key: Simple { key: "coords", py_key: Py( 0x00007fa14b021b60, ), path: LookupPath( [ S( "coords", Py( 0x00007fa14b021b90, ), ), ], ), }, name_py: Py( 0x00007fa14bd723a0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "selection", lookup_key: Simple { key: "selection", py_key: Py( 0x00007fa14ae735b0, ), path: LookupPath( [ S( "selection", Py( 0x00007fa14ae71730, ), ), ], ), }, name_py: Py( 0x00007fa14bd7bbf0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "storage", lookup_key: Simple { key: "storage", py_key: Py( 0x00007fa14b021bc0, ), path: LookupPath( [ S( "storage", Py( 0x00007fa14b021e60, ), ), ], ), }, name_py: Py( 0x00007fa14d76d020, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb03571000, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "ascii": 0, "binary": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c6ab0, ), Py( 0x00007fa14b3c6b10, ), ], }, missing: None, expected_repr: "'ascii' or 'binary'", strict: false, class_repr: "StlStorage", name: "str-enum[StlStorage]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b021e90, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b021dd0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd71ce0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "stl": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd71ce0, ), ], }, expected_repr: "'stl'", name: "literal['stl']", }, ), validate_default: false, copy_default: false, name: "default[literal['stl']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "units", lookup_key: Simple { key: "units", py_key: Py( 0x00007fa14b021da0, ), path: LookupPath( [ S( "units", Py( 0x00007fa14b021cb0, ), ), ], ), }, name_py: Py( 0x00007fa14e6aeac0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "OptionStl", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb02f4b6a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionStl", }, ), ], }, from_attributes: true, strict: false, custom_error: None, tags_repr: "'fbx', 'gltf', 'obj', 'ply', 'step', 'stl'", discriminator_repr: "'type'", name: "tagged-union[OptionFbx,OptionGltf,OptionObj,OptionPly,OptionStep,OptionStl]", }, ), class: Py( 0x000055eb0365a4c0, ), post_init: None, frozen: false, custom_init: false, root_model: true, undefined: Py( 0x00007fa14d802350, ), name: "RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b021c80, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b021ce0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14e164660, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "export": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14e164660, ), ], }, expected_repr: "'export'", name: "literal['export']", }, ), validate_default: false, copy_default: false, name: "default[literal['export']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionExport", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a3ed70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionExport", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "type", lookup_key: Simple { key: "type", py_key: Py(0x7fa14b021710), path: LookupPath([S("type", Py(0x7fa14b0212c0))]) }, name_py: Py(0x7fa14fa3c5a0), validator: WithDefault(WithDefaultValidator { default: Default(Py(0x7fa14bb4ecf0)), on_error: Raise, validator: Literal(LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"default_scene": 0}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7fa14bb4ecf0)] }, expected_repr: "'default_scene'", name: "literal['default_scene']" }), validate_default: false, copy_default: false, name: "default[literal['default_scene']]", undefined: Py(0x7fa14d802350) }), frozen: false }], model_name: "OptionDefaultScene", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb035cf600), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "OptionDefaultScene" }), Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "index", lookup_key: Simple { key: "index", py_key: Py(0x7fa14b021170), path: LookupPath([S("index", Py(0x7fa14b021110))]) }, name_py: Py(0x7fa14f9faac0), validator: Int(IntValidator { strict: false }), frozen: false }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py(0x7fa14b0210b0), path: LookupPath([S("type", Py(0x7fa14b021080))]) }, name_py: Py(0x7fa14fa3c5a0), validator: WithDefault(WithDefaultValidator { default: Default(Py(0x7fa14bb51570)), on_error: Raise, validator: Literal(LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"mesh_by_index": 0}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7fa14bb51570)] }, expected_repr: "'mesh_by_index'", name: "literal['mesh_by_index']" }), validate_default: false, copy_default: false, name: "default[literal['mesh_by_index']]", undefined: Py(0x7fa14d802350) }), frozen: false }], model_name: "OptionMeshByIndex", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb035d2c80), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "OptionMeshByIndex" }), StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x55eb02f8b040), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"z": 1, "y": 0}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7fa14b3c4e90), Py(0x7fa14b3c4ef0)] }, missing: None, expected_repr: "'y' or 'z'", strict: false, class_repr: "Axis", name: "str-enum[Axis]" }), StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x55eb034c76d0), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"in": 2, "mm": 4, "m": 3, "cm": 0, "yd": 5, "ft": 1}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7fa14b3c4c50), Py(0x7fa14b3c51f0), Py(0x7fa14b3c52b0), Py(0x7fa14b3c5310), Py(0x7fa14b3c5370), Py(0x7fa14b3c53d0)] }, missing: None, expected_repr: "'cm', 'ft', 'in', 'm', 'mm' or 'yd'", strict: false, class_repr: "UnitLength", name: "str-enum[UnitLength]" }), Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "axis", lookup_key: Simple { key: "axis", py_key: Py(0x7fa14afe7d50), path: LookupPath([S("axis", Py(0x7fa14afe7300))]) }, name_py: Py(0x7fa14c13dce0), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }, Field { name: "direction", lookup_key: Simple { key: "direction", py_key: Py(0x7fa14ae40630), path: LookupPath([S("direction", Py(0x7fa14b287330))]) }, name_py: Py(0x7fa14e41ef30), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }], model_name: "AxisDirectionPair", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb02f8c440), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "AxisDirectionPair" }), Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "forward", lookup_key: Simple { key: "forward", py_key: Py(0x7fa14afe7120), path: LookupPath([S("forward", Py(0x7fa14afe7e70))]) }, name_py: Py(0x7fa14bd71cb0), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py(0x7fa14afe72d0), path: LookupPath([S("up", Py(0x7fa14afe7ea0))]) }, name_py: Py(0x7fa14d9ed0b0), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }], model_name: "System", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb034cb8e0), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "System" }), Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "name", lookup_key: Simple { key: "name", py_key: Py(0x7fa14b021380), path: LookupPath([S("name", Py(0x7fa14b020fc0))]) }, name_py: Py(0x7fa14fa3a388), validator: Str(StrValidator { strict: false, coerce_numbers_to_str: false }), frozen: false }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py(0x7fa14b021020), path: LookupPath([S("type", Py(0x7fa14b020ff0))]) }, name_py: Py(0x7fa14fa3c5a0), validator: WithDefault(WithDefaultValidator { default: Default(Py(0x7fa14bb4cd70)), on_error: Raise, validator: Literal(LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"scene_by_name": 0}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7fa14bb4cd70)] }, expected_repr: "'scene_by_name'", name: "literal['scene_by_name']" }), validate_default: false, copy_default: false, name: "default[literal['scene_by_name']]", undefined: Py(0x7fa14d802350) }), frozen: false }], model_name: "OptionSceneByName", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb035d72d0), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "OptionSceneByName" }), Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "name", lookup_key: Simple { key: "name", py_key: Py(0x7fa14b0210e0), path: LookupPath([S("name", Py(0x7fa14b020480))]) }, name_py: Py(0x7fa14fa3a388), validator: Str(StrValidator { strict: false, coerce_numbers_to_str: false }), frozen: false }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py(0x7fa14b020ba0), path: LookupPath([S("type", Py(0x7fa14b020540))]) }, name_py: Py(0x7fa14fa3c5a0), validator: WithDefault(WithDefaultValidator { default: Default(Py(0x7fa14bb516f0)), on_error: Raise, validator: Literal(LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"mesh_by_name": 0}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7fa14bb516f0)] }, expected_repr: "'mesh_by_name'", name: "literal['mesh_by_name']" }), validate_default: false, copy_default: false, name: "default[literal['mesh_by_name']]", undefined: Py(0x7fa14d802350) }), frozen: false }], model_name: "OptionMeshByName", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb03568b70), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "OptionMeshByName" }), Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "index", lookup_key: Simple { key: "index", py_key: Py(0x7fa14b021350), path: LookupPath([S("index", Py(0x7fa14b021200))]) }, name_py: Py(0x7fa14f9faac0), validator: Int(IntValidator { strict: false }), frozen: false }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py(0x7fa14b021230), path: LookupPath([S("type", Py(0x7fa14b0211a0))]) }, name_py: Py(0x7fa14fa3c5a0), validator: WithDefault(WithDefaultValidator { default: Default(Py(0x7fa14bb4caf0)), on_error: Raise, validator: Literal(LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"scene_by_index": 0}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7fa14bb4caf0)] }, expected_repr: "'scene_by_index'", name: "literal['scene_by_index']" }), validate_default: false, copy_default: false, name: "default[literal['scene_by_index']]", undefined: Py(0x7fa14d802350) }), frozen: false }], model_name: "OptionSceneByIndex", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb035d53c0), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "OptionSceneByIndex" }), StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x55eb02f8ba40), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"negative": 1, "positive": 0}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7fa14b3c5010), Py(0x7fa14b3c5070)] }, missing: None, expected_repr: "'positive' or 'negative'", strict: false, class_repr: "Direction", name: "str-enum[Direction]" }), Model(ModelValidator { revalidate: Never, validator: TaggedUnion(TaggedUnionValidator { discriminator: LookupKey(Simple { key: "type", py_key: Py(0x7fa14fa3c5a0), path: LookupPath([S("type", Py(0x7fa14fa3c5a0))]) }), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"scene_by_name": 2, "mesh_by_index": 3, "scene_by_index": 1, "mesh_by_name": 4, "default_scene": 0}), expected_py_dict: None, expected_py_values: None, values: [DefinitionRef(DefinitionRefValidator { definition: "OptionDefaultScene" }), DefinitionRef(DefinitionRefValidator { definition: "OptionSceneByIndex" }), DefinitionRef(DefinitionRefValidator { definition: "OptionSceneByName" }), DefinitionRef(DefinitionRefValidator { definition: "OptionMeshByIndex" }), DefinitionRef(DefinitionRefValidator { definition: "OptionMeshByName" })] }, from_attributes: true, strict: false, custom_error: None, tags_repr: "'default_scene', 'scene_by_index', 'scene_by_name', 'mesh_by_index', 'mesh_by_name'", discriminator_repr: "'type'", name: "tagged-union[OptionDefaultScene,OptionSceneByIndex,OptionSceneByName,OptionMeshByIndex,OptionMeshByName]" }), class: Py(0x55eb033f6d30), post_init: None, frozen: false, custom_init: false, root_model: true, undefined: Py(0x7fa14d802350), name: "RootModel[Annotated[Union[OptionDefaultScene, OptionSceneByIndex, OptionSceneByName, OptionMeshByIndex, OptionMeshByName], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entity_ids: List[str], format: pydantic.root_model.RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]], type: Literal['export'] = 'export') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
-
format:
RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]][source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entity_ids': FieldInfo(annotation=List[str], required=True), 'format': FieldInfo(annotation=RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]], required=True), 'type': FieldInfo(annotation=Literal['export'], required=False, default='export')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionExtendPath(**data)[source][source]
Extend a path by adding a new segment which starts at the path’s “pen”. If no “pen” location has been set before (via
MovePen), then the pen is at the origin.Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'path': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'segment': <class 'pydantic.root_model.RootModel[Annotated[Union[OptionLine, OptionArc, OptionBezier, OptionTangentialArc, OptionTangentialArcTo, OptionArcTo], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, 'type': typing.Literal['extend_path']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <class 'kittycad.models.angle.Angle'>, 'config': {'title': 'Angle'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.angle.Angle'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.angle.Angle'>>]}, 'ref': 'kittycad.models.angle.Angle:94467862185984', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.unit_angle.UnitAngle:94467859501968', 'type': 'definition-ref'}, 'type': 'model-field'}, 'value': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Angle', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <enum 'UnitAngle'>, 'members': [UnitAngle.DEGREES, UnitAngle.RADIANS], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_angle.UnitAngle:94467859501968', 'sub_type': 'str', 'type': 'enum'}], 'schema': {'cls': <class 'kittycad.models.modeling_cmd.OptionExtendPath'>, 'config': {'title': 'OptionExtendPath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionExtendPath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionExtendPath'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionExtendPath:94467866450288', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'path': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'type': 'model-field'}, 'segment': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'pydantic.root_model.RootModel[Annotated[Union[OptionLine, OptionArc, OptionBezier, OptionTangentialArc, OptionTangentialArcTo, OptionArcTo], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, 'config': {'title': "RootModel[Annotated[Union[OptionLine, OptionArc, OptionBezier, OptionTangentialArc, OptionTangentialArcTo, OptionArcTo], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]"}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'pydantic.root_model.RootModel[Annotated[Union[OptionLine, OptionArc, OptionBezier, OptionTangentialArc, OptionTangentialArcTo, OptionArcTo], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'pydantic.root_model.RootModel[Annotated[Union[OptionLine, OptionArc, OptionBezier, OptionTangentialArc, OptionTangentialArcTo, OptionArcTo], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>>]}, 'ref': "pydantic.root_model.RootModel:94467864067568[Annotated[Union[OptionLine, OptionArc, OptionBezier, OptionTangentialArc, OptionTangentialArcTo, OptionArcTo], FieldInfo(annotation=NoneType, required=True, discriminator='type')]:140330724558272]", 'root_model': True, 'schema': {'choices': {'arc': {'cls': <class 'kittycad.models.path_segment.OptionArc'>, 'config': {'title': 'OptionArc'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_segment.OptionArc'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_segment.OptionArc'>>]}, 'ref': 'kittycad.models.path_segment.OptionArc:94467866394144', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'end': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.angle.Angle:94467862185984', 'type': 'definition-ref'}, 'type': 'model-field'}, 'radius': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'relative': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'start': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.angle.Angle:94467862185984', 'type': 'definition-ref'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'arc', 'schema': {'expected': ['arc'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionArc', 'type': 'model-fields'}, 'type': 'model'}, 'arc_to': {'cls': <class 'kittycad.models.path_segment.OptionArcTo'>, 'config': {'title': 'OptionArcTo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_segment.OptionArcTo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_segment.OptionArcTo'>>]}, 'ref': 'kittycad.models.path_segment.OptionArcTo:94467866154896', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'end': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'interior': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'relative': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'arc_to', 'schema': {'expected': ['arc_to'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionArcTo', 'type': 'model-fields'}, 'type': 'model'}, 'bezier': {'cls': <class 'kittycad.models.path_segment.OptionBezier'>, 'config': {'title': 'OptionBezier'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_segment.OptionBezier'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_segment.OptionBezier'>>]}, 'ref': 'kittycad.models.path_segment.OptionBezier:94467866101312', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'control1': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'control2': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'end': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'relative': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'bezier', 'schema': {'expected': ['bezier'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionBezier', 'type': 'model-fields'}, 'type': 'model'}, 'line': {'cls': <class 'kittycad.models.path_segment.OptionLine'>, 'config': {'title': 'OptionLine'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_segment.OptionLine'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_segment.OptionLine'>>]}, 'ref': 'kittycad.models.path_segment.OptionLine:94467866382016', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'end': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'relative': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'line', 'schema': {'expected': ['line'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionLine', 'type': 'model-fields'}, 'type': 'model'}, 'tangential_arc': {'cls': <class 'kittycad.models.path_segment.OptionTangentialArc'>, 'config': {'title': 'OptionTangentialArc'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_segment.OptionTangentialArc'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_segment.OptionTangentialArc'>>]}, 'ref': 'kittycad.models.path_segment.OptionTangentialArc:94467866120720', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'offset': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.angle.Angle:94467862185984', 'type': 'definition-ref'}, 'type': 'model-field'}, 'radius': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'tangential_arc', 'schema': {'expected': ['tangential_arc'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionTangentialArc', 'type': 'model-fields'}, 'type': 'model'}, 'tangential_arc_to': {'cls': <class 'kittycad.models.path_segment.OptionTangentialArcTo'>, 'config': {'title': 'OptionTangentialArcTo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_segment.OptionTangentialArcTo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_segment.OptionTangentialArcTo'>>]}, 'ref': 'kittycad.models.path_segment.OptionTangentialArcTo:94467866134800', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'angle_snap_increment': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'schema_ref': 'kittycad.models.angle.Angle:94467862185984', 'type': 'definition-ref'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'to': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'tangential_arc_to', 'schema': {'expected': ['tangential_arc_to'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionTangentialArcTo', 'type': 'model-fields'}, 'type': 'model'}}, 'discriminator': 'type', 'from_attributes': True, 'metadata': {}, 'strict': False, 'type': 'tagged-union'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'extend_path', 'schema': {'expected': ['extend_path'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionExtendPath', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb039f5970, ), serializer: Fields( GeneralFieldsSerializer { fields: { "path": SerField { key_py: Py( 0x00007fa14fa3ac30, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bf7e630, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "extend_path", }, expected_py: None, name: "literal['extend_path']", }, ), }, ), ), required: true, }, "segment": SerField { key_py: Py( 0x00007fa14e9da1f0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb039b2310, ), serializer: TaggedUnion( TaggedUnionSerializer { discriminator: LookupKey( Simple { key: "type", py_key: Py( 0x00007fa14fa3c5a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14fa3c5a0, ), ), ], ), }, ), lookup: { "arc_to": 5, "line": 0, "bezier": 2, "arc": 1, "tangential_arc": 3, "tangential_arc_to": 4, }, choices: [ Model( ModelSerializer { class: Py( 0x000055eb039e4ec0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "end": SerField { key_py: Py( 0x00007fa14fa38368, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14fa39c38, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "line", }, expected_py: None, name: "literal['line']", }, ), }, ), ), required: true, }, "relative": SerField { key_py: Py( 0x00007fa14e010bf0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionLine", }, ), Model( ModelSerializer { class: Py( 0x000055eb039e7e20, ), serializer: Fields( GeneralFieldsSerializer { fields: { "start": SerField { key_py: Py( 0x00007fa14fa3bce8, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "center": SerField { key_py: Py( 0x00007fa14ef08150, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), ), required: true, }, "relative": SerField { key_py: Py( 0x00007fa14e010bf0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "end": SerField { key_py: Py( 0x00007fa14fa38368, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14cf81350, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "arc", }, expected_py: None, name: "literal['arc']", }, ), }, ), ), required: true, }, "radius": SerField { key_py: Py( 0x00007fa14b020cf0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 6, }, ), has_extra: false, root_model: false, name: "OptionArc", }, ), Model( ModelSerializer { class: Py( 0x000055eb039a0640, ), serializer: Fields( GeneralFieldsSerializer { fields: { "control1": SerField { key_py: Py( 0x00007fa14afff3f0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "end": SerField { key_py: Py( 0x00007fa14fa38368, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "relative": SerField { key_py: Py( 0x00007fa14e010bf0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "control2": SerField { key_py: Py( 0x00007fa14b01dff0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14d8e7330, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "bezier", }, expected_py: None, name: "literal['bezier']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "OptionBezier", }, ), Model( ModelSerializer { class: Py( 0x000055eb039a5210, ), serializer: Fields( GeneralFieldsSerializer { fields: { "radius": SerField { key_py: Py( 0x00007fa14b020cf0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14b01e230, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "tangential_arc", }, expected_py: None, name: "literal['tangential_arc']", }, ), }, ), ), required: true, }, "offset": SerField { key_py: Py( 0x00007fa14fa3a7c0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionTangentialArc", }, ), Model( ModelSerializer { class: Py( 0x000055eb039a8910, ), serializer: Fields( GeneralFieldsSerializer { fields: { "angle_snap_increment": SerField { key_py: Py( 0x00007fa14b01e3f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), }, ), }, ), ), required: true, }, "to": SerField { key_py: Py( 0x00007fa14eb8b120, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14ae2e6f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "tangential_arc_to", }, expected_py: None, name: "literal['tangential_arc_to']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionTangentialArcTo", }, ), Model( ModelSerializer { class: Py( 0x000055eb039ad790, ), serializer: Fields( GeneralFieldsSerializer { fields: { "interior": SerField { key_py: Py( 0x00007fa14ae2e7b0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14afd1740, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "arc_to", }, expected_py: None, name: "literal['arc_to']", }, ), }, ), ), required: true, }, "relative": SerField { key_py: Py( 0x00007fa14e010bf0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "end": SerField { key_py: Py( 0x00007fa14fa38368, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "OptionArcTo", }, ), ], name: "TaggedUnion[OptionLine, OptionArc, OptionBezier, OptionTangentialArc, OptionTangentialArcTo, OptionArcTo]", }, ), has_extra: false, root_model: true, name: "RootModel[Annotated[Union[OptionLine, OptionArc, OptionBezier, OptionTangentialArc, OptionTangentialArcTo, OptionArcTo], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionExtendPath", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" }), Enum(EnumSerializer { class: Py(0x55eb03355390), serializer: Some(Str(StrSerializer)) }), Model(ModelSerializer { class: Py(0x55eb035e4800), serializer: Fields(GeneralFieldsSerializer { fields: {"unit": SerField { key_py: Py(0x7fa14e161380), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: false })), required: true }, "value": SerField { key_py: Py(0x7fa14fa3c768), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 2 }), has_extra: false, root_model: false, name: "Angle" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionExtendPath", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "path", lookup_key: Simple { key: "path", py_key: Py( 0x00007fa14afd37b0, ), path: LookupPath( [ S( "path", Py( 0x00007fa14afd3e40, ), ), ], ), }, name_py: Py( 0x00007fa14fa3ac30, ), validator: FunctionAfter( FunctionAfterValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), func: Py( 0x000055eb039991a0, ), config: Py( 0x00007fa14ae41940, ), name: "function-after[ModelingCmdId(), str]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "segment", lookup_key: Simple { key: "segment", py_key: Py( 0x00007fa14afe41e0, ), path: LookupPath( [ S( "segment", Py( 0x00007fa14afe4360, ), ), ], ), }, name_py: Py( 0x00007fa14e9da1f0, ), validator: Model( ModelValidator { revalidate: Never, validator: TaggedUnion( TaggedUnionValidator { discriminator: LookupKey( Simple { key: "type", py_key: Py( 0x00007fa14fa3c5a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14fa3c5a0, ), ), ], ), }, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "line": 0, "arc_to": 5, "bezier": 2, "tangential_arc_to": 4, "tangential_arc": 3, "arc": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "end", lookup_key: Simple { key: "end", py_key: Py( 0x00007fa14afd3720, ), path: LookupPath( [ S( "end", Py( 0x00007fa14afd2bb0, ), ), ], ), }, name_py: Py( 0x00007fa14fa38368, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "relative", lookup_key: Simple { key: "relative", py_key: Py( 0x00007fa14afd5bf0, ), path: LookupPath( [ S( "relative", Py( 0x00007fa14afbfc70, ), ), ], ), }, name_py: Py( 0x00007fa14e010bf0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afd3690, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afd3f00, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14fa39c38, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "line": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14fa39c38, ), ], }, expected_repr: "'line'", name: "literal['line']", }, ), validate_default: false, copy_default: false, name: "default[literal['line']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionLine", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039e4ec0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionLine", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007fa14afd36f0, ), path: LookupPath( [ S( "center", Py( 0x00007fa14afd3360, ), ), ], ), }, name_py: Py( 0x00007fa14ef08150, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae434c0, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae434c0, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), frozen: false, }, Field { name: "end", lookup_key: Simple { key: "end", py_key: Py( 0x00007fa14afd3480, ), path: LookupPath( [ S( "end", Py( 0x00007fa14afd3060, ), ), ], ), }, name_py: Py( 0x00007fa14fa38368, ), validator: DefinitionRef( DefinitionRefValidator { definition: "Angle", }, ), frozen: false, }, Field { name: "radius", lookup_key: Simple { key: "radius", py_key: Py( 0x00007fa14afd3660, ), path: LookupPath( [ S( "radius", Py( 0x00007fa14afd3570, ), ), ], ), }, name_py: Py( 0x00007fa14b020cf0, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae42f80, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "relative", lookup_key: Simple { key: "relative", py_key: Py( 0x00007fa14afbf9f0, ), path: LookupPath( [ S( "relative", Py( 0x00007fa14afbfbb0, ), ), ], ), }, name_py: Py( 0x00007fa14e010bf0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "start", lookup_key: Simple { key: "start", py_key: Py( 0x00007fa14afd0450, ), path: LookupPath( [ S( "start", Py( 0x00007fa14afd2c70, ), ), ], ), }, name_py: Py( 0x00007fa14fa3bce8, ), validator: DefinitionRef( DefinitionRefValidator { definition: "Angle", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe5650, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe5710, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14cf81350, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "arc": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14cf81350, ), ], }, expected_repr: "'arc'", name: "literal['arc']", }, ), validate_default: false, copy_default: false, name: "default[literal['arc']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionArc", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039e7e20, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionArc", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "control1", lookup_key: Simple { key: "control1", py_key: Py( 0x00007fa14afbfcf0, ), path: LookupPath( [ S( "control1", Py( 0x00007fa14afbf670, ), ), ], ), }, name_py: Py( 0x00007fa14afff3f0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "control2", lookup_key: Simple { key: "control2", py_key: Py( 0x00007fa14af91670, ), path: LookupPath( [ S( "control2", Py( 0x00007fa14af90130, ), ), ], ), }, name_py: Py( 0x00007fa14b01dff0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "end", lookup_key: Simple { key: "end", py_key: Py( 0x00007fa14afe5530, ), path: LookupPath( [ S( "end", Py( 0x00007fa14afe5560, ), ), ], ), }, name_py: Py( 0x00007fa14fa38368, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "relative", lookup_key: Simple { key: "relative", py_key: Py( 0x00007fa14af911f0, ), path: LookupPath( [ S( "relative", Py( 0x00007fa14af929f0, ), ), ], ), }, name_py: Py( 0x00007fa14e010bf0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe54a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe5950, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14d8e7330, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "bezier": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14d8e7330, ), ], }, expected_repr: "'bezier'", name: "literal['bezier']", }, ), validate_default: false, copy_default: false, name: "default[literal['bezier']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionBezier", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039a0640, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionBezier", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "offset", lookup_key: Simple { key: "offset", py_key: Py( 0x00007fa14afe5380, ), path: LookupPath( [ S( "offset", Py( 0x00007fa14afe53e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3a7c0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "Angle", }, ), frozen: false, }, Field { name: "radius", lookup_key: Simple { key: "radius", py_key: Py( 0x00007fa14afe53b0, ), path: LookupPath( [ S( "radius", Py( 0x00007fa14afe54d0, ), ), ], ), }, name_py: Py( 0x00007fa14b020cf0, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae42600, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe5470, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe5410, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14b01e230, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "tangential_arc": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b01e230, ), ], }, expected_repr: "'tangential_arc'", name: "literal['tangential_arc']", }, ), validate_default: false, copy_default: false, name: "default[literal['tangential_arc']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionTangentialArc", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039a5210, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionTangentialArc", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "angle_snap_increment", lookup_key: Simple { key: "angle_snap_increment", py_key: Py( 0x00007fa14af92ab0, ), path: LookupPath( [ S( "angle_snap_increment", Py( 0x00007fa14ae70c70, ), ), ], ), }, name_py: Py( 0x00007fa14b01e3f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: DefinitionRef( DefinitionRefValidator { definition: "Angle", }, ), name: "nullable[Angle]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Angle]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "to", lookup_key: Simple { key: "to", py_key: Py( 0x00007fa14afe5350, ), path: LookupPath( [ S( "to", Py( 0x00007fa14afe5440, ), ), ], ), }, name_py: Py( 0x00007fa14eb8b120, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe5020, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe50b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14ae2e6f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "tangential_arc_to": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14ae2e6f0, ), ], }, expected_repr: "'tangential_arc_to'", name: "literal['tangential_arc_to']", }, ), validate_default: false, copy_default: false, name: "default[literal['tangential_arc_to']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionTangentialArcTo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039a8910, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionTangentialArcTo", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "end", lookup_key: Simple { key: "end", py_key: Py( 0x00007fa14afe4f00, ), path: LookupPath( [ S( "end", Py( 0x00007fa14afe4f30, ), ), ], ), }, name_py: Py( 0x00007fa14fa38368, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "interior", lookup_key: Simple { key: "interior", py_key: Py( 0x00007fa14ae70a30, ), path: LookupPath( [ S( "interior", Py( 0x00007fa14ae70630, ), ), ], ), }, name_py: Py( 0x00007fa14ae2e7b0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "relative", lookup_key: Simple { key: "relative", py_key: Py( 0x00007fa14ae701b0, ), path: LookupPath( [ S( "relative", Py( 0x00007fa14ae70270, ), ), ], ), }, name_py: Py( 0x00007fa14e010bf0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe4390, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe50e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14afd1740, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "arc_to": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14afd1740, ), ], }, expected_repr: "'arc_to'", name: "literal['arc_to']", }, ), validate_default: false, copy_default: false, name: "default[literal['arc_to']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionArcTo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039ad790, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionArcTo", }, ), ], }, from_attributes: true, strict: false, custom_error: None, tags_repr: "'line', 'arc', 'bezier', 'tangential_arc', 'tangential_arc_to', 'arc_to'", discriminator_repr: "'type'", name: "tagged-union[OptionLine,OptionArc,OptionBezier,OptionTangentialArc,OptionTangentialArcTo,OptionArcTo]", }, ), class: Py( 0x000055eb039b2310, ), post_init: None, frozen: false, custom_init: false, root_model: true, undefined: Py( 0x00007fa14d802350, ), name: "RootModel[Annotated[Union[OptionLine, OptionArc, OptionBezier, OptionTangentialArc, OptionTangentialArcTo, OptionArcTo], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe4240, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe42a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bf7e630, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "extend_path": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bf7e630, ), ], }, expected_repr: "'extend_path'", name: "literal['extend_path']", }, ), validate_default: false, copy_default: false, name: "default[literal['extend_path']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionExtendPath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039f5970, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionExtendPath", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "unit", lookup_key: Simple { key: "unit", py_key: Py(0x7fa14afd3d80), path: LookupPath([S("unit", Py(0x7fa14afd3c30))]) }, name_py: Py(0x7fa14e161380), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }, Field { name: "value", lookup_key: Simple { key: "value", py_key: Py(0x7fa14afd38d0), path: LookupPath([S("value", Py(0x7fa14afd3f60))]) }, name_py: Py(0x7fa14fa3c768), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Angle", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb035e4800), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Angle" }), Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" }), StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x55eb03355390), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"degrees": 0, "radians": 1}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7fa14b361850), Py(0x7fa14b3618b0)] }, missing: None, expected_repr: "'degrees' or 'radians'", strict: false, class_repr: "UnitAngle", name: "str-enum[UnitAngle]" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, path: kittycad.models.modeling_cmd_id.ModelingCmdId, segment: pydantic.root_model.RootModel[Annotated[Union[OptionLine, OptionArc, OptionBezier, OptionTangentialArc, OptionTangentialArcTo, OptionArcTo], FieldInfo(annotation=NoneType, required=True, discriminator='type')]], type: Literal['extend_path'] = 'extend_path') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'path': FieldInfo(annotation=ModelingCmdId, required=True), 'segment': FieldInfo(annotation=RootModel[Annotated[Union[OptionLine, OptionArc, OptionBezier, OptionTangentialArc, OptionTangentialArcTo, OptionArcTo], FieldInfo(annotation=NoneType, required=True, discriminator='type')]], required=True), 'type': FieldInfo(annotation=Literal['extend_path'], required=False, default='extend_path')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
-
path:
ModelingCmdId[source]
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
- class kittycad.models.modeling_cmd.OptionExtrude(**data)[source][source]
Command for extruding a solid 2d.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'distance': <class 'kittycad.models.length_unit.LengthUnit'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'target': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'type': typing.Literal['extrude']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionExtrude'>, 'config': {'title': 'OptionExtrude'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionExtrude'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionExtrude'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionExtrude:94467866464800', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'distance': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'target': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'extrude', 'schema': {'expected': ['extrude'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionExtrude', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb039f9220, ), serializer: Fields( GeneralFieldsSerializer { fields: { "target": SerField { key_py: Py( 0x00007fa14fa3c088, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14c15c990, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "extrude", }, expected_py: None, name: "literal['extrude']", }, ), }, ), ), required: true, }, "distance": SerField { key_py: Py( 0x00007fa14e8a9730, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionExtrude", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionExtrude", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "distance", lookup_key: Simple { key: "distance", py_key: Py( 0x00007fa14b123570, ), path: LookupPath( [ S( "distance", Py( 0x00007fa14b099ff0, ), ), ], ), }, name_py: Py( 0x00007fa14e8a9730, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14afd69c0, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "target", lookup_key: Simple { key: "target", py_key: Py( 0x00007fa14afe5b00, ), path: LookupPath( [ S( "target", Py( 0x00007fa14afe5a10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c088, ), validator: FunctionAfter( FunctionAfterValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), func: Py( 0x000055eb039991a0, ), config: Py( 0x00007fa14afd69c0, ), name: "function-after[ModelingCmdId(), str]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe5aa0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe59b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14c15c990, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "extrude": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14c15c990, ), ], }, expected_repr: "'extrude'", name: "literal['extrude']", }, ), validate_default: false, copy_default: false, name: "default[literal['extrude']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionExtrude", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039f9220, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionExtrude", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, distance: kittycad.models.length_unit.LengthUnit, target: kittycad.models.modeling_cmd_id.ModelingCmdId, type: Literal['extrude'] = 'extrude') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
-
distance:
LengthUnit[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'distance': FieldInfo(annotation=LengthUnit, required=True), 'target': FieldInfo(annotation=ModelingCmdId, required=True), 'type': FieldInfo(annotation=Literal['extrude'], required=False, default='extrude')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
target:
ModelingCmdId[source]
- class kittycad.models.modeling_cmd.OptionFaceGetCenter(**data)[source][source]
Obtains the surface “center of mass”
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['face_get_center']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionFaceGetCenter'>, 'config': {'title': 'OptionFaceGetCenter'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionFaceGetCenter'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionFaceGetCenter'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionFaceGetCenter:94467867535552', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'face_get_center', 'schema': {'expected': ['face_get_center'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionFaceGetCenter', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03afe8c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4e7b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "face_get_center", }, expected_py: None, name: "literal['face_get_center']", }, ), }, ), ), required: true, }, "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionFaceGetCenter", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionFaceGetCenter", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14ad3e1f0, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14ad3e170, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae74390, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae743c0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4e7b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "face_get_center": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4e7b0, ), ], }, expected_repr: "'face_get_center'", name: "literal['face_get_center']", }, ), validate_default: false, copy_default: false, name: "default[literal['face_get_center']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionFaceGetCenter", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03afe8c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionFaceGetCenter", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, object_id: str, type: Literal['face_get_center'] = 'face_get_center') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['face_get_center'], required=False, default='face_get_center')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionFaceGetGradient(**data)[source][source]
Determines the gradient (dFdu, dFdv) + normal vector on a brep face evaluated by parameters u,v
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['face_get_gradient'], 'uv': <class 'kittycad.models.point2d.Point2d'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionFaceGetGradient'>, 'config': {'title': 'OptionFaceGetGradient'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionFaceGetGradient'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionFaceGetGradient'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionFaceGetGradient:94467867550000', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'face_get_gradient', 'schema': {'expected': ['face_get_gradient'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'uv': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'OptionFaceGetGradient', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b02130, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4e870, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "face_get_gradient", }, expected_py: None, name: "literal['face_get_gradient']", }, ), }, ), ), required: true, }, "uv": SerField { key_py: Py( 0x00007fa14b021140, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), ), required: true, }, "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionFaceGetGradient", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionFaceGetGradient", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14ad3fdb0, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14ad3fd70, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae76d60, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae76d90, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4e870, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "face_get_gradient": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4e870, ), ], }, expected_repr: "'face_get_gradient'", name: "literal['face_get_gradient']", }, ), validate_default: false, copy_default: false, name: "default[literal['face_get_gradient']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "uv", lookup_key: Simple { key: "uv", py_key: Py( 0x00007fa14ae76dc0, ), path: LookupPath( [ S( "uv", Py( 0x00007fa14ae76df0, ), ), ], ), }, name_py: Py( 0x00007fa14b021140, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ad3fc40, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ad3fc40, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), frozen: false, }, ], model_name: "OptionFaceGetGradient", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b02130, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionFaceGetGradient", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, object_id: str, type: Literal['face_get_gradient'] = 'face_get_gradient', uv: kittycad.models.point2d.Point2d) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['face_get_gradient'], required=False, default='face_get_gradient'), 'uv': FieldInfo(annotation=Point2d, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionFaceGetPosition(**data)[source][source]
Determines a position on a brep face evaluated by parameters u,v
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['face_get_position'], 'uv': <class 'kittycad.models.point2d.Point2d'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionFaceGetPosition'>, 'config': {'title': 'OptionFaceGetPosition'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionFaceGetPosition'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionFaceGetPosition'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionFaceGetPosition:94467867526848', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'face_get_position', 'schema': {'expected': ['face_get_position'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'uv': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'OptionFaceGetPosition', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03afc6c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "uv": SerField { key_py: Py( 0x00007fa14b021140, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), ), required: true, }, "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4e930, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "face_get_position", }, expected_py: None, name: "literal['face_get_position']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionFaceGetPosition", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionFaceGetPosition", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14ad3d3f0, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14ad3d3b0, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae76760, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae76940, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4e930, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "face_get_position": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4e930, ), ], }, expected_repr: "'face_get_position'", name: "literal['face_get_position']", }, ), validate_default: false, copy_default: false, name: "default[literal['face_get_position']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "uv", lookup_key: Simple { key: "uv", py_key: Py( 0x00007fa14ae769a0, ), path: LookupPath( [ S( "uv", Py( 0x00007fa14ae76730, ), ), ], ), }, name_py: Py( 0x00007fa14b021140, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ad3d280, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ad3d280, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), frozen: false, }, ], model_name: "OptionFaceGetPosition", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03afc6c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionFaceGetPosition", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, object_id: str, type: Literal['face_get_position'] = 'face_get_position', uv: kittycad.models.point2d.Point2d) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['face_get_position'], required=False, default='face_get_position'), 'uv': FieldInfo(annotation=Point2d, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionFaceIsPlanar(**data)[source][source]
Determines whether a brep face is planar and returns its surface-local planar axes if so
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['face_is_planar']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionFaceIsPlanar'>, 'config': {'title': 'OptionFaceIsPlanar'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionFaceIsPlanar'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionFaceIsPlanar'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionFaceIsPlanar:94467867492016', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'face_is_planar', 'schema': {'expected': ['face_is_planar'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionFaceIsPlanar', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03af3eb0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4e9b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "face_is_planar", }, expected_py: None, name: "literal['face_is_planar']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionFaceIsPlanar", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionFaceIsPlanar", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14ad2ff30, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14ad2feb0, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae751d0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae75200, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4e9b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "face_is_planar": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4e9b0, ), ], }, expected_repr: "'face_is_planar'", name: "literal['face_is_planar']", }, ), validate_default: false, copy_default: false, name: "default[literal['face_is_planar']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionFaceIsPlanar", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03af3eb0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionFaceIsPlanar", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, object_id: str, type: Literal['face_is_planar'] = 'face_is_planar') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['face_is_planar'], required=False, default='face_is_planar')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionGetEntityType(**data)[source][source]
What type of entity is this?
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entity_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['get_entity_type']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionGetEntityType'>, 'config': {'title': 'OptionGetEntityType'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionGetEntityType'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionGetEntityType'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionGetEntityType:94467867382160', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'get_entity_type', 'schema': {'expected': ['get_entity_type'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionGetEntityType', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ad9190, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4f870, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "get_entity_type", }, expected_py: None, name: "literal['get_entity_type']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionGetEntityType", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionGetEntityType", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14af103b0, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14af10370, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b027750, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b027780, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4f870, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "get_entity_type": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4f870, ), ], }, expected_repr: "'get_entity_type'", name: "literal['get_entity_type']", }, ), validate_default: false, copy_default: false, name: "default[literal['get_entity_type']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionGetEntityType", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ad9190, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionGetEntityType", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entity_id: str, type: Literal['get_entity_type'] = 'get_entity_type') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entity_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['get_entity_type'], required=False, default='get_entity_type')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionGetNumObjects(**data)[source][source]
Get the number of objects in the scene
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['get_num_objects']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionGetNumObjects'>, 'config': {'title': 'OptionGetNumObjects'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionGetNumObjects'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionGetNumObjects'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionGetNumObjects:94467868279568', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'get_num_objects', 'schema': {'expected': ['get_num_objects'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionGetNumObjects', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03bb4310, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4f8f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "get_num_objects", }, expected_py: None, name: "literal['get_num_objects']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionGetNumObjects", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionGetNumObjects", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae022e0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae02310, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4f8f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "get_num_objects": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4f8f0, ), ], }, expected_repr: "'get_num_objects'", name: "literal['get_num_objects']", }, ), validate_default: false, copy_default: false, name: "default[literal['get_num_objects']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionGetNumObjects", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03bb4310, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionGetNumObjects", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['get_num_objects'] = 'get_num_objects') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['get_num_objects'], required=False, default='get_num_objects')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionGetSketchModePlane(**data)[source][source]
Get the plane for sketch mode.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['get_sketch_mode_plane']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionGetSketchModePlane'>, 'config': {'title': 'OptionGetSketchModePlane'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionGetSketchModePlane'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionGetSketchModePlane'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionGetSketchModePlane:94467867681216', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'get_sketch_mode_plane', 'schema': {'expected': ['get_sketch_mode_plane'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionGetSketchModePlane', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b221c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4f9b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "get_sketch_mode_plane", }, expected_py: None, name: "literal['get_sketch_mode_plane']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionGetSketchModePlane", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionGetSketchModePlane", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad54cf0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad54d20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4f9b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "get_sketch_mode_plane": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4f9b0, ), ], }, expected_repr: "'get_sketch_mode_plane'", name: "literal['get_sketch_mode_plane']", }, ), validate_default: false, copy_default: false, name: "default[literal['get_sketch_mode_plane']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionGetSketchModePlane", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b221c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionGetSketchModePlane", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['get_sketch_mode_plane'] = 'get_sketch_mode_plane') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['get_sketch_mode_plane'], required=False, default='get_sketch_mode_plane')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionHandleMouseDragEnd(**data)[source][source]
Stop dragging the mouse.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['handle_mouse_drag_end'], 'window': <class 'kittycad.models.point2d.Point2d'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionHandleMouseDragEnd'>, 'config': {'title': 'OptionHandleMouseDragEnd'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionHandleMouseDragEnd'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionHandleMouseDragEnd'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionHandleMouseDragEnd:94467867884448', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'handle_mouse_drag_end', 'schema': {'expected': ['handle_mouse_drag_end'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'window': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'OptionHandleMouseDragEnd', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b53ba0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4ff70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "handle_mouse_drag_end", }, expected_py: None, name: "literal['handle_mouse_drag_end']", }, ), }, ), ), required: true, }, "window": SerField { key_py: Py( 0x00007fa14e1645a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionHandleMouseDragEnd", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionHandleMouseDragEnd", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad56b80, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad56bb0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4ff70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "handle_mouse_drag_end": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4ff70, ), ], }, expected_repr: "'handle_mouse_drag_end'", name: "literal['handle_mouse_drag_end']", }, ), validate_default: false, copy_default: false, name: "default[literal['handle_mouse_drag_end']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "window", lookup_key: Simple { key: "window", py_key: Py( 0x00007fa14ad564f0, ), path: LookupPath( [ S( "window", Py( 0x00007fa14ad571b0, ), ), ], ), }, name_py: Py( 0x00007fa14e1645a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ad9e340, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ad9e340, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), frozen: false, }, ], model_name: "OptionHandleMouseDragEnd", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b53ba0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionHandleMouseDragEnd", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['handle_mouse_drag_end'] = 'handle_mouse_drag_end', window: kittycad.models.point2d.Point2d) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['handle_mouse_drag_end'], required=False, default='handle_mouse_drag_end'), 'window': FieldInfo(annotation=Point2d, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionHandleMouseDragMove(**data)[source][source]
Continue dragging the mouse.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'sequence': typing.Optional[int], 'type': typing.Literal['handle_mouse_drag_move'], 'window': <class 'kittycad.models.point2d.Point2d'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionHandleMouseDragMove'>, 'config': {'title': 'OptionHandleMouseDragMove'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionHandleMouseDragMove'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionHandleMouseDragMove'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionHandleMouseDragMove:94467867870672', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'sequence': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'handle_mouse_drag_move', 'schema': {'expected': ['handle_mouse_drag_move'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'window': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'OptionHandleMouseDragMove', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b505d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "sequence": SerField { key_py: Py( 0x00007fa14fa3b790, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Int( IntSerializer, ), }, ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4fe30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "handle_mouse_drag_move", }, expected_py: None, name: "literal['handle_mouse_drag_move']", }, ), }, ), ), required: true, }, "window": SerField { key_py: Py( 0x00007fa14e1645a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionHandleMouseDragMove", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionHandleMouseDragMove", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "sequence", lookup_key: Simple { key: "sequence", py_key: Py( 0x00007fa14ad646f0, ), path: LookupPath( [ S( "sequence", Py( 0x00007fa14ad9dd30, ), ), ], ), }, name_py: Py( 0x00007fa14fa3b790, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Int( IntValidator { strict: false, }, ), name: "nullable[int]", }, ), validate_default: false, copy_default: false, name: "default[nullable[int]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad57f90, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad57f60, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4fe30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "handle_mouse_drag_move": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4fe30, ), ], }, expected_repr: "'handle_mouse_drag_move'", name: "literal['handle_mouse_drag_move']", }, ), validate_default: false, copy_default: false, name: "default[literal['handle_mouse_drag_move']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "window", lookup_key: Simple { key: "window", py_key: Py( 0x00007fa14ad57f30, ), path: LookupPath( [ S( "window", Py( 0x00007fa14ad57c90, ), ), ], ), }, name_py: Py( 0x00007fa14e1645a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14adca600, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14adca600, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), frozen: false, }, ], model_name: "OptionHandleMouseDragMove", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b505d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionHandleMouseDragMove", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, sequence: Optional[int] = None, type: Literal['handle_mouse_drag_move'] = 'handle_mouse_drag_move', window: kittycad.models.point2d.Point2d) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'sequence': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['handle_mouse_drag_move'], required=False, default='handle_mouse_drag_move'), 'window': FieldInfo(annotation=Point2d, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionHandleMouseDragStart(**data)[source][source]
Start dragging the mouse.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['handle_mouse_drag_start'], 'window': <class 'kittycad.models.point2d.Point2d'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionHandleMouseDragStart'>, 'config': {'title': 'OptionHandleMouseDragStart'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionHandleMouseDragStart'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionHandleMouseDragStart'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionHandleMouseDragStart:94467867862048', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'handle_mouse_drag_start', 'schema': {'expected': ['handle_mouse_drag_start'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'window': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'OptionHandleMouseDragStart', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b4e420, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4fc70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "handle_mouse_drag_start", }, expected_py: None, name: "literal['handle_mouse_drag_start']", }, ), }, ), ), required: true, }, "window": SerField { key_py: Py( 0x00007fa14e1645a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionHandleMouseDragStart", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionHandleMouseDragStart", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc4360, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc4390, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4fc70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "handle_mouse_drag_start": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4fc70, ), ], }, expected_repr: "'handle_mouse_drag_start'", name: "literal['handle_mouse_drag_start']", }, ), validate_default: false, copy_default: false, name: "default[literal['handle_mouse_drag_start']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "window", lookup_key: Simple { key: "window", py_key: Py( 0x00007fa14adc43c0, ), path: LookupPath( [ S( "window", Py( 0x00007fa14adc43f0, ), ), ], ), }, name_py: Py( 0x00007fa14e1645a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14adc8940, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14adc8940, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), frozen: false, }, ], model_name: "OptionHandleMouseDragStart", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b4e420, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionHandleMouseDragStart", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['handle_mouse_drag_start'] = 'handle_mouse_drag_start', window: kittycad.models.point2d.Point2d) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['handle_mouse_drag_start'], required=False, default='handle_mouse_drag_start'), 'window': FieldInfo(annotation=Point2d, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionHighlightSetEntities(**data)[source][source]
Changes the current highlighted entity to these entities.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entities': typing.List[str], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['highlight_set_entities']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionHighlightSetEntities'>, 'config': {'title': 'OptionHighlightSetEntities'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionHighlightSetEntities'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionHighlightSetEntities'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionHighlightSetEntities:94467867216432', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entities': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'highlight_set_entities', 'schema': {'expected': ['highlight_set_entities'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionHighlightSetEntities', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ab0a30, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be3a9b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "highlight_set_entities", }, expected_py: None, name: "literal['highlight_set_entities']", }, ), }, ), ), required: true, }, "entities": SerField { key_py: Py( 0x00007fa14d502070, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionHighlightSetEntities", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionHighlightSetEntities", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entities", lookup_key: Simple { key: "entities", py_key: Py( 0x00007fa14aee72f0, ), path: LookupPath( [ S( "entities", Py( 0x00007fa14aee7770, ), ), ], ), }, name_py: Py( 0x00007fa14d502070, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b0256e0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b0256b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be3a9b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "highlight_set_entities": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be3a9b0, ), ], }, expected_repr: "'highlight_set_entities'", name: "literal['highlight_set_entities']", }, ), validate_default: false, copy_default: false, name: "default[literal['highlight_set_entities']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionHighlightSetEntities", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ab0a30, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionHighlightSetEntities", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entities: List[str], type: Literal['highlight_set_entities'] = 'highlight_set_entities') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entities': FieldInfo(annotation=List[str], required=True), 'type': FieldInfo(annotation=Literal['highlight_set_entities'], required=False, default='highlight_set_entities')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionHighlightSetEntity(**data)[source][source]
Changes the current highlighted entity to whichever one is at the given window coordinate. If there’s no entity at this location, clears the highlight.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'selected_at_window': <class 'kittycad.models.point2d.Point2d'>, 'sequence': typing.Optional[int], 'type': typing.Literal['highlight_set_entity']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionHighlightSetEntity'>, 'config': {'title': 'OptionHighlightSetEntity'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionHighlightSetEntity'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionHighlightSetEntity'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionHighlightSetEntity:94467867207200', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'selected_at_window': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'sequence': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'highlight_set_entity', 'schema': {'expected': ['highlight_set_entity'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionHighlightSetEntity', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03aae620, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be3a3b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "highlight_set_entity", }, expected_py: None, name: "literal['highlight_set_entity']", }, ), }, ), ), required: true, }, "selected_at_window": SerField { key_py: Py( 0x00007fa14aff6df0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), ), required: true, }, "sequence": SerField { key_py: Py( 0x00007fa14fa3b790, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Int( IntSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionHighlightSetEntity", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionHighlightSetEntity", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "selected_at_window", lookup_key: Simple { key: "selected_at_window", py_key: Py( 0x00007fa14aef02b0, ), path: LookupPath( [ S( "selected_at_window", Py( 0x00007fa14aef0270, ), ), ], ), }, name_py: Py( 0x00007fa14aff6df0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14aee7ec0, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14aee7ec0, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), frozen: false, }, Field { name: "sequence", lookup_key: Simple { key: "sequence", py_key: Py( 0x00007fa14aef0330, ), path: LookupPath( [ S( "sequence", Py( 0x00007fa14aef02f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3b790, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Int( IntValidator { strict: false, }, ), name: "nullable[int]", }, ), validate_default: false, copy_default: false, name: "default[nullable[int]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b025ec0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b025ef0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be3a3b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "highlight_set_entity": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be3a3b0, ), ], }, expected_repr: "'highlight_set_entity'", name: "literal['highlight_set_entity']", }, ), validate_default: false, copy_default: false, name: "default[literal['highlight_set_entity']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionHighlightSetEntity", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03aae620, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionHighlightSetEntity", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, selected_at_window: kittycad.models.point2d.Point2d, sequence: Optional[int] = None, type: Literal['highlight_set_entity'] = 'highlight_set_entity') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'selected_at_window': FieldInfo(annotation=Point2d, required=True), 'sequence': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['highlight_set_entity'], required=False, default='highlight_set_entity')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionImportFiles(**data)[source][source]
Import files to the current model.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'files': typing.List[kittycad.models.import_file.ImportFile], 'format': <class 'pydantic.root_model.RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionSldprt, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['import_files']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>, 'config': {'title': 'AxisDirectionPair'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>>]}, 'ref': 'kittycad.models.axis_direction_pair.AxisDirectionPair:94467855533120', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.axis.Axis:94467855528000', 'type': 'definition-ref'}, 'type': 'model-field'}, 'direction': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.direction.Direction:94467855530560', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'AxisDirectionPair', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <enum 'Axis'>, 'members': [Axis.Y, Axis.Z], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.axis.Axis:94467855528000', 'sub_type': 'str', 'type': 'enum'}, {'cls': <enum 'Direction'>, 'members': [Direction.POSITIVE, Direction.NEGATIVE], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.direction.Direction:94467855530560', 'sub_type': 'str', 'type': 'enum'}, {'cls': <class 'kittycad.models.system.System'>, 'config': {'title': 'System'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.system.System'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.system.System'>>]}, 'ref': 'kittycad.models.system.System:94467861035232', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'forward': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.axis_direction_pair.AxisDirectionPair:94467855533120', 'type': 'definition-ref'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.axis_direction_pair.AxisDirectionPair:94467855533120', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'System', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <enum 'UnitLength'>, 'members': [UnitLength.CM, UnitLength.FT, UnitLength.IN, UnitLength.M, UnitLength.MM, UnitLength.YD], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_length.UnitLength:94467861018320', 'sub_type': 'str', 'type': 'enum'}], 'schema': {'cls': <class 'kittycad.models.modeling_cmd.OptionImportFiles'>, 'config': {'title': 'OptionImportFiles'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionImportFiles'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionImportFiles'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionImportFiles:94467867946048', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'files': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'cls': <class 'kittycad.models.import_file.ImportFile'>, 'config': {'title': 'ImportFile'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.import_file.ImportFile'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.import_file.ImportFile'>>]}, 'ref': 'kittycad.models.import_file.ImportFile:94467850099936', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bytes'}, 'type': 'model-field'}, 'path': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'ImportFile', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}, 'format': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'pydantic.root_model.RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionSldprt, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, 'config': {'title': "RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionSldprt, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]"}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'pydantic.root_model.RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionSldprt, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'pydantic.root_model.RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionSldprt, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>>]}, 'ref': "pydantic.root_model.RootModel:94467864067568[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionSldprt, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]:140330738859808]", 'root_model': True, 'schema': {'choices': {'fbx': {'cls': <class 'kittycad.models.input_format.OptionFbx'>, 'config': {'title': 'OptionFbx'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.OptionFbx'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.OptionFbx'>>]}, 'ref': 'kittycad.models.input_format.OptionFbx:94467861021664', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'fbx', 'schema': {'expected': ['fbx'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionFbx', 'type': 'model-fields'}, 'type': 'model'}, 'gltf': {'cls': <class 'kittycad.models.input_format.OptionGltf'>, 'config': {'title': 'OptionGltf'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.OptionGltf'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.OptionGltf'>>]}, 'ref': 'kittycad.models.input_format.OptionGltf:94467862042928', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'gltf', 'schema': {'expected': ['gltf'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionGltf', 'type': 'model-fields'}, 'type': 'model'}, 'obj': {'cls': <class 'kittycad.models.input_format.OptionObj'>, 'config': {'title': 'OptionObj'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.OptionObj'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.OptionObj'>>]}, 'ref': 'kittycad.models.input_format.OptionObj:94467862048624', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'coords': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.system.System:94467861035232', 'type': 'definition-ref'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'obj', 'schema': {'expected': ['obj'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'units': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.unit_length.UnitLength:94467861018320', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'OptionObj', 'type': 'model-fields'}, 'type': 'model'}, 'ply': {'cls': <class 'kittycad.models.input_format.OptionPly'>, 'config': {'title': 'OptionPly'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.OptionPly'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.OptionPly'>>]}, 'ref': 'kittycad.models.input_format.OptionPly:94467855604016', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'coords': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.system.System:94467861035232', 'type': 'definition-ref'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'ply', 'schema': {'expected': ['ply'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'units': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.unit_length.UnitLength:94467861018320', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'OptionPly', 'type': 'model-fields'}, 'type': 'model'}, 'sldprt': {'cls': <class 'kittycad.models.input_format.OptionSldprt'>, 'config': {'title': 'OptionSldprt'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.OptionSldprt'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.OptionSldprt'>>]}, 'ref': 'kittycad.models.input_format.OptionSldprt:94467860978304', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'split_closed_faces': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'sldprt', 'schema': {'expected': ['sldprt'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSldprt', 'type': 'model-fields'}, 'type': 'model'}, 'step': {'cls': <class 'kittycad.models.input_format.OptionStep'>, 'config': {'title': 'OptionStep'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.OptionStep'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.OptionStep'>>]}, 'ref': 'kittycad.models.input_format.OptionStep:94467860988128', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'split_closed_faces': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'step', 'schema': {'expected': ['step'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionStep', 'type': 'model-fields'}, 'type': 'model'}, 'stl': {'cls': <class 'kittycad.models.input_format.OptionStl'>, 'config': {'title': 'OptionStl'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.OptionStl'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.OptionStl'>>]}, 'ref': 'kittycad.models.input_format.OptionStl:94467860998144', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'coords': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.system.System:94467861035232', 'type': 'definition-ref'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'stl', 'schema': {'expected': ['stl'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'units': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.unit_length.UnitLength:94467861018320', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'OptionStl', 'type': 'model-fields'}, 'type': 'model'}}, 'discriminator': 'type', 'from_attributes': True, 'metadata': {}, 'strict': False, 'type': 'tagged-union'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'import_files', 'schema': {'expected': ['import_files'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionImportFiles', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b62c40, ), serializer: Fields( GeneralFieldsSerializer { fields: { "format": SerField { key_py: Py( 0x00007fa14fa38b50, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb02f8f8e0, ), serializer: TaggedUnion( TaggedUnionSerializer { discriminator: LookupKey( Simple { key: "type", py_key: Py( 0x00007fa14fa3c5a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14fa3c5a0, ), ), ], ), }, ), lookup: { "stl": 6, "sldprt": 4, "fbx": 0, "gltf": 1, "obj": 2, "ply": 3, "step": 5, }, choices: [ Model( ModelSerializer { class: Py( 0x000055eb034c83e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd71170, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "fbx", }, expected_py: None, name: "literal['fbx']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionFbx", }, ), Model( ModelSerializer { class: Py( 0x000055eb035c1930, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd710e0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "gltf", }, expected_py: None, name: "literal['gltf']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionGltf", }, ), Model( ModelSerializer { class: Py( 0x000055eb035c2f70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14fa3a760, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "obj", }, expected_py: None, name: "literal['obj']", }, ), }, ), ), required: true, }, "units": SerField { key_py: Py( 0x00007fa14e6aeac0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: false, }, ), ), required: true, }, "coords": SerField { key_py: Py( 0x00007fa14bd723a0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionObj", }, ), Model( ModelSerializer { class: Py( 0x000055eb02f9d930, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd71b00, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "ply", }, expected_py: None, name: "literal['ply']", }, ), }, ), ), required: true, }, "coords": SerField { key_py: Py( 0x00007fa14bd723a0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "units": SerField { key_py: Py( 0x00007fa14e6aeac0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: false, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionPly", }, ), Model( ModelSerializer { class: Py( 0x000055eb034bda80, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd71da0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "sldprt", }, expected_py: None, name: "literal['sldprt']", }, ), }, ), ), required: true, }, "split_closed_faces": SerField { key_py: Py( 0x00007fa14bb052b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f930d20, ), ), serializer: Bool( BoolSerializer, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSldprt", }, ), Model( ModelSerializer { class: Py( 0x000055eb034c00e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "split_closed_faces": SerField { key_py: Py( 0x00007fa14bb052b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f930d20, ), ), serializer: Bool( BoolSerializer, ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14fa3be10, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "step", }, expected_py: None, name: "literal['step']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionStep", }, ), Model( ModelSerializer { class: Py( 0x000055eb034c2800, ), serializer: Fields( GeneralFieldsSerializer { fields: { "coords": SerField { key_py: Py( 0x00007fa14bd723a0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd71ce0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "stl", }, expected_py: None, name: "literal['stl']", }, ), }, ), ), required: true, }, "units": SerField { key_py: Py( 0x00007fa14e6aeac0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: false, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionStl", }, ), ], name: "TaggedUnion[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionSldprt, OptionStep, OptionStl]", }, ), has_extra: false, root_model: true, name: "RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionSldprt, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc6b870, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "import_files", }, expected_py: None, name: "literal['import_files']", }, ), }, ), ), required: true, }, "files": SerField { key_py: Py( 0x00007fa14f9f0980, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Model( ModelSerializer { class: Py( 0x000055eb02a5dce0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Bytes( BytesSerializer { bytes_mode: Utf8, }, ), ), required: true, }, "path": SerField { key_py: Py( 0x00007fa14fa3ac30, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "ImportFile", }, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[ImportFile]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionImportFiles", }, ), definitions=[Enum(EnumSerializer { class: Py(0x55eb02f8b040), serializer: Some(Str(StrSerializer)) }), Model(ModelSerializer { class: Py(0x55eb034cb8e0), serializer: Fields(GeneralFieldsSerializer { fields: {"forward": SerField { key_py: Py(0x7fa14bd71cb0), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: true })), required: true }, "up": SerField { key_py: Py(0x7fa14d9ed0b0), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: true })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 2 }), has_extra: false, root_model: false, name: "System" }), Enum(EnumSerializer { class: Py(0x55eb034c76d0), serializer: Some(Str(StrSerializer)) }), Enum(EnumSerializer { class: Py(0x55eb02f8ba40), serializer: Some(Str(StrSerializer)) }), Model(ModelSerializer { class: Py(0x55eb02f8c440), serializer: Fields(GeneralFieldsSerializer { fields: {"axis": SerField { key_py: Py(0x7fa14c13dce0), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: false })), required: true }, "direction": SerField { key_py: Py(0x7fa14e41ef30), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: false })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 2 }), has_extra: false, root_model: false, name: "AxisDirectionPair" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionImportFiles", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "files", lookup_key: Simple { key: "files", py_key: Py( 0x00007fa14adc4930, ), path: LookupPath( [ S( "files", Py( 0x00007fa14adc4a50, ), ), ], ), }, name_py: Py( 0x00007fa14f9f0980, ), validator: List( ListValidator { strict: false, item_validator: Some( Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14adc4ea0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14adc5290, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Bytes( BytesValidator { strict: false, bytes_mode: ValBytesMode { ser: Utf8, }, }, ), frozen: false, }, Field { name: "path", lookup_key: Simple { key: "path", py_key: Py( 0x00007fa14adc52f0, ), path: LookupPath( [ S( "path", Py( 0x00007fa14adc4f60, ), ), ], ), }, name_py: Py( 0x00007fa14fa3ac30, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "ImportFile", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb02a5dce0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "ImportFile", }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "format", lookup_key: Simple { key: "format", py_key: Py( 0x00007fa14adc4720, ), path: LookupPath( [ S( "format", Py( 0x00007fa14adc5950, ), ), ], ), }, name_py: Py( 0x00007fa14fa38b50, ), validator: Model( ModelValidator { revalidate: Never, validator: TaggedUnion( TaggedUnionValidator { discriminator: LookupKey( Simple { key: "type", py_key: Py( 0x00007fa14fa3c5a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14fa3c5a0, ), ), ], ), }, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "step": 5, "obj": 2, "gltf": 1, "fbx": 0, "stl": 6, "ply": 3, "sldprt": 4, }, ), expected_py_dict: None, expected_py_values: None, values: [ Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc4d80, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc4de0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd71170, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "fbx": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd71170, ), ], }, expected_repr: "'fbx'", name: "literal['fbx']", }, ), validate_default: false, copy_default: false, name: "default[literal['fbx']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionFbx", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb034c83e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionFbx", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc5020, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc4cc0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd710e0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "gltf": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd710e0, ), ], }, expected_repr: "'gltf'", name: "literal['gltf']", }, ), validate_default: false, copy_default: false, name: "default[literal['gltf']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionGltf", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb035c1930, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionGltf", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "coords", lookup_key: Simple { key: "coords", py_key: Py( 0x00007fa14adc4ed0, ), path: LookupPath( [ S( "coords", Py( 0x00007fa14adc4e40, ), ), ], ), }, name_py: Py( 0x00007fa14bd723a0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc4c90, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc4db0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14fa3a760, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "obj": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14fa3a760, ), ], }, expected_repr: "'obj'", name: "literal['obj']", }, ), validate_default: false, copy_default: false, name: "default[literal['obj']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "units", lookup_key: Simple { key: "units", py_key: Py( 0x00007fa14adc4d50, ), path: LookupPath( [ S( "units", Py( 0x00007fa14adc4e10, ), ), ], ), }, name_py: Py( 0x00007fa14e6aeac0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "OptionObj", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb035c2f70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionObj", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "coords", lookup_key: Simple { key: "coords", py_key: Py( 0x00007fa14adc4cf0, ), path: LookupPath( [ S( "coords", Py( 0x00007fa14adc4060, ), ), ], ), }, name_py: Py( 0x00007fa14bd723a0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc40f0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc4120, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd71b00, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "ply": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd71b00, ), ], }, expected_repr: "'ply'", name: "literal['ply']", }, ), validate_default: false, copy_default: false, name: "default[literal['ply']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "units", lookup_key: Simple { key: "units", py_key: Py( 0x00007fa14adc4990, ), path: LookupPath( [ S( "units", Py( 0x00007fa14adc4660, ), ), ], ), }, name_py: Py( 0x00007fa14e6aeac0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "OptionPly", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb02f9d930, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPly", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "split_closed_faces", lookup_key: Simple { key: "split_closed_faces", py_key: Py( 0x00007fa14af843b0, ), path: LookupPath( [ S( "split_closed_faces", Py( 0x00007fa14ad2e970, ), ), ], ), }, name_py: Py( 0x00007fa14bb052b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f930d20, ), ), on_error: Raise, validator: Bool( BoolValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[bool]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc45d0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc4600, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd71da0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "sldprt": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd71da0, ), ], }, expected_repr: "'sldprt'", name: "literal['sldprt']", }, ), validate_default: false, copy_default: false, name: "default[literal['sldprt']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSldprt", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb034bda80, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSldprt", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "split_closed_faces", lookup_key: Simple { key: "split_closed_faces", py_key: Py( 0x00007fa14ad6e4f0, ), path: LookupPath( [ S( "split_closed_faces", Py( 0x00007fa14add0a30, ), ), ], ), }, name_py: Py( 0x00007fa14bb052b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f930d20, ), ), on_error: Raise, validator: Bool( BoolValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[bool]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc4240, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc41b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14fa3be10, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "step": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14fa3be10, ), ], }, expected_repr: "'step'", name: "literal['step']", }, ), validate_default: false, copy_default: false, name: "default[literal['step']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionStep", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb034c00e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionStep", }, ), Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "coords", lookup_key: Simple { key: "coords", py_key: Py( 0x00007fa14adc4300, ), path: LookupPath( [ S( "coords", Py( 0x00007fa14adc4150, ), ), ], ), }, name_py: Py( 0x00007fa14bd723a0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc4180, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc4480, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd71ce0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "stl": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd71ce0, ), ], }, expected_repr: "'stl'", name: "literal['stl']", }, ), validate_default: false, copy_default: false, name: "default[literal['stl']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "units", lookup_key: Simple { key: "units", py_key: Py( 0x00007fa14adc47e0, ), path: LookupPath( [ S( "units", Py( 0x00007fa14adc4900, ), ), ], ), }, name_py: Py( 0x00007fa14e6aeac0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "OptionStl", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb034c2800, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionStl", }, ), ], }, from_attributes: true, strict: false, custom_error: None, tags_repr: "'fbx', 'gltf', 'obj', 'ply', 'sldprt', 'step', 'stl'", discriminator_repr: "'type'", name: "tagged-union[OptionFbx,OptionGltf,OptionObj,OptionPly,OptionSldprt,OptionStep,OptionStl]", }, ), class: Py( 0x000055eb02f8f8e0, ), post_init: None, frozen: false, custom_init: false, root_model: true, undefined: Py( 0x00007fa14d802350, ), name: "RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionSldprt, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc5980, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc59b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc6b870, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "import_files": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc6b870, ), ], }, expected_repr: "'import_files'", name: "literal['import_files']", }, ), validate_default: false, copy_default: false, name: "default[literal['import_files']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionImportFiles", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b62c40, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionImportFiles", }, ), definitions=[StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x55eb034c76d0), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"ft": 1, "m": 3, "mm": 4, "yd": 5, "cm": 0, "in": 2}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7fa14b3c4c50), Py(0x7fa14b3c51f0), Py(0x7fa14b3c52b0), Py(0x7fa14b3c5310), Py(0x7fa14b3c5370), Py(0x7fa14b3c53d0)] }, missing: None, expected_repr: "'cm', 'ft', 'in', 'm', 'mm' or 'yd'", strict: false, class_repr: "UnitLength", name: "str-enum[UnitLength]" }), StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x55eb02f8b040), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"y": 0, "z": 1}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7fa14b3c4e90), Py(0x7fa14b3c4ef0)] }, missing: None, expected_repr: "'y' or 'z'", strict: false, class_repr: "Axis", name: "str-enum[Axis]" }), Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "forward", lookup_key: Simple { key: "forward", py_key: Py(0x7fa14adc55c0), path: LookupPath([S("forward", Py(0x7fa14adc51d0))]) }, name_py: Py(0x7fa14bd71cb0), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py(0x7fa14adc5320), path: LookupPath([S("up", Py(0x7fa14adc5380))]) }, name_py: Py(0x7fa14d9ed0b0), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }], model_name: "System", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb034cb8e0), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "System" }), Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "axis", lookup_key: Simple { key: "axis", py_key: Py(0x7fa14adc5350), path: LookupPath([S("axis", Py(0x7fa14adc53e0))]) }, name_py: Py(0x7fa14c13dce0), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }, Field { name: "direction", lookup_key: Simple { key: "direction", py_key: Py(0x7fa14add22f0), path: LookupPath([S("direction", Py(0x7fa14add3c30))]) }, name_py: Py(0x7fa14e41ef30), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }], model_name: "AxisDirectionPair", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb02f8c440), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "AxisDirectionPair" }), StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x55eb02f8ba40), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"positive": 0, "negative": 1}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7fa14b3c5010), Py(0x7fa14b3c5070)] }, missing: None, expected_repr: "'positive' or 'negative'", strict: false, class_repr: "Direction", name: "str-enum[Direction]" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, files: List[kittycad.models.import_file.ImportFile], format: pydantic.root_model.RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionSldprt, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]], type: Literal['import_files'] = 'import_files') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
-
files:
List[ImportFile][source]
-
format:
RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionSldprt, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]][source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'files': FieldInfo(annotation=List[ImportFile], required=True), 'format': FieldInfo(annotation=RootModel[Annotated[Union[OptionFbx, OptionGltf, OptionObj, OptionPly, OptionSldprt, OptionStep, OptionStl], FieldInfo(annotation=NoneType, required=True, discriminator='type')]], required=True), 'type': FieldInfo(annotation=Literal['import_files'], required=False, default='import_files')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionLoft(**data)[source][source]
Command for lofting sections to create a solid
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'base_curve_index': typing.Optional[int], 'bez_approximate_rational': <class 'bool'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'section_ids': typing.List[str], 'tolerance': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': typing.Literal['loft'], 'v_degree': <class 'int'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionLoft'>, 'config': {'title': 'OptionLoft'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionLoft'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionLoft'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionLoft:94467866601168', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'base_curve_index': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'bez_approximate_rational': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'section_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'tolerance': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'loft', 'schema': {'expected': ['loft'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'v_degree': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}}, 'model_name': 'OptionLoft', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a1a6d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "section_ids": SerField { key_py: Py( 0x00007fa14d6a0bf0, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14c15eee0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "loft", }, expected_py: None, name: "literal['loft']", }, ), }, ), ), required: true, }, "base_curve_index": SerField { key_py: Py( 0x00007fa14afe0ab0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Int( IntSerializer, ), }, ), }, ), ), required: true, }, "tolerance": SerField { key_py: Py( 0x00007fa14afd8570, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "bez_approximate_rational": SerField { key_py: Py( 0x00007fa14afb79b0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "v_degree": SerField { key_py: Py( 0x00007fa14afe0b70, ), alias: None, alias_py: None, serializer: Some( Int( IntSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 6, }, ), has_extra: false, root_model: false, name: "OptionLoft", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionLoft", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "base_curve_index", lookup_key: Simple { key: "base_curve_index", py_key: Py( 0x00007fa14ae3a6f0, ), path: LookupPath( [ S( "base_curve_index", Py( 0x00007fa14ae3a5f0, ), ), ], ), }, name_py: Py( 0x00007fa14afe0ab0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Int( IntValidator { strict: false, }, ), name: "nullable[int]", }, ), validate_default: false, copy_default: false, name: "default[nullable[int]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "bez_approximate_rational", lookup_key: Simple { key: "bez_approximate_rational", py_key: Py( 0x00007fa14b009020, ), path: LookupPath( [ S( "bez_approximate_rational", Py( 0x00007fa14b00a150, ), ), ], ), }, name_py: Py( 0x00007fa14afb79b0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "section_ids", lookup_key: Simple { key: "section_ids", py_key: Py( 0x00007fa14ae3a530, ), path: LookupPath( [ S( "section_ids", Py( 0x00007fa14ae3a5b0, ), ), ], ), }, name_py: Py( 0x00007fa14d6a0bf0, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "tolerance", lookup_key: Simple { key: "tolerance", py_key: Py( 0x00007fa14ae3acb0, ), path: LookupPath( [ S( "tolerance", Py( 0x00007fa14ae3b130, ), ), ], ), }, name_py: Py( 0x00007fa14afd8570, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae3a080, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe4b70, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe4cc0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14c15eee0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "loft": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14c15eee0, ), ], }, expected_repr: "'loft'", name: "literal['loft']", }, ), validate_default: false, copy_default: false, name: "default[literal['loft']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "v_degree", lookup_key: Simple { key: "v_degree", py_key: Py( 0x00007fa14ae3a1f0, ), path: LookupPath( [ S( "v_degree", Py( 0x00007fa14ae3a1b0, ), ), ], ), }, name_py: Py( 0x00007fa14afe0b70, ), validator: Int( IntValidator { strict: false, }, ), frozen: false, }, ], model_name: "OptionLoft", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a1a6d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionLoft", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, base_curve_index: Optional[int] = None, bez_approximate_rational: bool, section_ids: List[str], tolerance: kittycad.models.length_unit.LengthUnit, type: Literal['loft'] = 'loft', v_degree: int) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'base_curve_index': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'bez_approximate_rational': FieldInfo(annotation=bool, required=True), 'section_ids': FieldInfo(annotation=List[str], required=True), 'tolerance': FieldInfo(annotation=LengthUnit, required=True), 'type': FieldInfo(annotation=Literal['loft'], required=False, default='loft'), 'v_degree': FieldInfo(annotation=int, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
tolerance:
LengthUnit[source]
- class kittycad.models.modeling_cmd.OptionMakeAxesGizmo(**data)[source][source]
Add a gizmo showing the axes.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'clobber': <class 'bool'>, 'gizmo_mode': <class 'bool'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['make_axes_gizmo']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionMakeAxesGizmo'>, 'config': {'title': 'OptionMakeAxesGizmo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionMakeAxesGizmo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionMakeAxesGizmo'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionMakeAxesGizmo:94467867809456', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'clobber': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'gizmo_mode': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'make_axes_gizmo', 'schema': {'expected': ['make_axes_gizmo'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMakeAxesGizmo', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b416b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc69c30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "make_axes_gizmo", }, expected_py: None, name: "literal['make_axes_gizmo']", }, ), }, ), ), required: true, }, "clobber": SerField { key_py: Py( 0x00007fa14afe6e20, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "gizmo_mode": SerField { key_py: Py( 0x00007fa14b01f930, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionMakeAxesGizmo", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMakeAxesGizmo", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "clobber", lookup_key: Simple { key: "clobber", py_key: Py( 0x00007fa14ad54120, ), path: LookupPath( [ S( "clobber", Py( 0x00007fa14ad556b0, ), ), ], ), }, name_py: Py( 0x00007fa14afe6e20, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "gizmo_mode", lookup_key: Simple { key: "gizmo_mode", py_key: Py( 0x00007fa14adaacf0, ), path: LookupPath( [ S( "gizmo_mode", Py( 0x00007fa14adaab70, ), ), ], ), }, name_py: Py( 0x00007fa14b01f930, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad55290, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad55680, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc69c30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "make_axes_gizmo": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc69c30, ), ], }, expected_repr: "'make_axes_gizmo'", name: "literal['make_axes_gizmo']", }, ), validate_default: false, copy_default: false, name: "default[literal['make_axes_gizmo']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionMakeAxesGizmo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b416b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionMakeAxesGizmo", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, clobber: bool, gizmo_mode: bool, type: Literal['make_axes_gizmo'] = 'make_axes_gizmo') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'clobber': FieldInfo(annotation=bool, required=True), 'gizmo_mode': FieldInfo(annotation=bool, required=True), 'type': FieldInfo(annotation=Literal['make_axes_gizmo'], required=False, default='make_axes_gizmo')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionMakeOffsetPath(**data)[source][source]
Make a new path by offsetting an object by a given distance. The new path’s ID will be the ID of this command.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'face_id': typing.Optional[str], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'offset': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': typing.Literal['make_offset_path']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionMakeOffsetPath'>, 'config': {'title': 'OptionMakeOffsetPath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionMakeOffsetPath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionMakeOffsetPath'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionMakeOffsetPath:94467868287136', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'face_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'offset': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'make_offset_path', 'schema': {'expected': ['make_offset_path'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMakeOffsetPath', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03bb60a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "face_id": SerField { key_py: Py( 0x00007fa14b32c8a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "offset": SerField { key_py: Py( 0x00007fa14fa3a7c0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc69f30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "make_offset_path", }, expected_py: None, name: "literal['make_offset_path']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "OptionMakeOffsetPath", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMakeOffsetPath", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "face_id", lookup_key: Simple { key: "face_id", py_key: Py( 0x00007fa14ae028e0, ), path: LookupPath( [ S( "face_id", Py( 0x00007fa14ae02910, ), ), ], ), }, name_py: Py( 0x00007fa14b32c8a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14af1dbb0, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14ae25f30, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "offset", lookup_key: Simple { key: "offset", py_key: Py( 0x00007fa14ae02940, ), path: LookupPath( [ S( "offset", Py( 0x00007fa14ae02970, ), ), ], ), }, name_py: Py( 0x00007fa14fa3a7c0, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae25ec0, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae029a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae029d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc69f30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "make_offset_path": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc69f30, ), ], }, expected_repr: "'make_offset_path'", name: "literal['make_offset_path']", }, ), validate_default: false, copy_default: false, name: "default[literal['make_offset_path']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionMakeOffsetPath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03bb60a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionMakeOffsetPath", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, face_id: Optional[str] = None, object_id: str, offset: kittycad.models.length_unit.LengthUnit, type: Literal['make_offset_path'] = 'make_offset_path') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'face_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'object_id': FieldInfo(annotation=str, required=True), 'offset': FieldInfo(annotation=LengthUnit, required=True), 'type': FieldInfo(annotation=Literal['make_offset_path'], required=False, default='make_offset_path')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
-
offset:
LengthUnit[source]
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionMakePlane(**data)[source][source]
Make a new plane
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'clobber': <class 'bool'>, 'hide': typing.Optional[bool], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'origin': <class 'kittycad.models.point3d.Point3d'>, 'size': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': typing.Literal['make_plane'], 'x_axis': <class 'kittycad.models.point3d.Point3d'>, 'y_axis': <class 'kittycad.models.point3d.Point3d'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.modeling_cmd.OptionMakePlane'>, 'config': {'title': 'OptionMakePlane'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionMakePlane'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionMakePlane'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionMakePlane:94467867591680', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'clobber': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'hide': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'bool'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'origin': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'make_plane', 'schema': {'expected': ['make_plane'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'x_axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'y_axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'OptionMakePlane', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b0c400, ), serializer: Fields( GeneralFieldsSerializer { fields: { "clobber": SerField { key_py: Py( 0x00007fa14afe6e20, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "origin": SerField { key_py: Py( 0x00007fa14fa3aa98, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "hide": SerField { key_py: Py( 0x00007fa14e246f10, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Bool( BoolSerializer, ), }, ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc68d30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "make_plane", }, expected_py: None, name: "literal['make_plane']", }, ), }, ), ), required: true, }, "y_axis": SerField { key_py: Py( 0x00007fa14b063030, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "size": SerField { key_py: Py( 0x00007fa14fa3ba40, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x_axis": SerField { key_py: Py( 0x00007fa14b062f40, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "OptionMakePlane", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMakePlane", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "clobber", lookup_key: Simple { key: "clobber", py_key: Py( 0x00007fa14ae779c0, ), path: LookupPath( [ S( "clobber", Py( 0x00007fa14ae779f0, ), ), ], ), }, name_py: Py( 0x00007fa14afe6e20, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "hide", lookup_key: Simple { key: "hide", py_key: Py( 0x00007fa14ae77a20, ), path: LookupPath( [ S( "hide", Py( 0x00007fa14ae77a50, ), ), ], ), }, name_py: Py( 0x00007fa14e246f10, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Bool( BoolValidator { strict: false, }, ), name: "nullable[bool]", }, ), validate_default: false, copy_default: false, name: "default[nullable[bool]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "origin", lookup_key: Simple { key: "origin", py_key: Py( 0x00007fa14ae77a80, ), path: LookupPath( [ S( "origin", Py( 0x00007fa14ae77ab0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3aa98, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "size", lookup_key: Simple { key: "size", py_key: Py( 0x00007fa14ae77ae0, ), path: LookupPath( [ S( "size", Py( 0x00007fa14ae77b10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3ba40, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ad50c80, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae77b40, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae77b70, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc68d30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "make_plane": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc68d30, ), ], }, expected_repr: "'make_plane'", name: "literal['make_plane']", }, ), validate_default: false, copy_default: false, name: "default[literal['make_plane']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "x_axis", lookup_key: Simple { key: "x_axis", py_key: Py( 0x00007fa14ae77ba0, ), path: LookupPath( [ S( "x_axis", Py( 0x00007fa14ae77bd0, ), ), ], ), }, name_py: Py( 0x00007fa14b062f40, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "y_axis", lookup_key: Simple { key: "y_axis", py_key: Py( 0x00007fa14ae77c00, ), path: LookupPath( [ S( "y_axis", Py( 0x00007fa14ae77c30, ), ), ], ), }, name_py: Py( 0x00007fa14b063030, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "OptionMakePlane", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b0c400, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionMakePlane", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, clobber: bool, hide: Optional[bool] = None, origin: kittycad.models.point3d.Point3d, size: kittycad.models.length_unit.LengthUnit, type: Literal['make_plane'] = 'make_plane', x_axis: kittycad.models.point3d.Point3d, y_axis: kittycad.models.point3d.Point3d) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'clobber': FieldInfo(annotation=bool, required=True), 'hide': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'origin': FieldInfo(annotation=Point3d, required=True), 'size': FieldInfo(annotation=LengthUnit, required=True), 'type': FieldInfo(annotation=Literal['make_plane'], required=False, default='make_plane'), 'x_axis': FieldInfo(annotation=Point3d, required=True), 'y_axis': FieldInfo(annotation=Point3d, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
size:
LengthUnit[source]
- class kittycad.models.modeling_cmd.OptionMass(**data)[source][source]
Get the mass of entities in the scene or the default scene.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entity_ids': typing.List[str], 'material_density': <class 'float'>, 'material_density_unit': <enum 'UnitDensity'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'output_unit': <enum 'UnitMass'>, 'type': typing.Literal['mass']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionMass'>, 'config': {'title': 'OptionMass'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionMass'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionMass'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionMass:94467868035824', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'material_density': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'material_density_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitDensity'>, 'members': [UnitDensity.LB_FT3, UnitDensity.KG_M3], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_density.UnitDensity:94467863217504', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitMass'>, 'members': [UnitMass.G, UnitMass.KG, UnitMass.LB], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_mass.UnitMass:94467863219280', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'mass', 'schema': {'expected': ['mass'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMass', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b78af0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007fa14b038970, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "material_density_unit": SerField { key_py: Py( 0x00007fa14bb4f8f0, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb036e0560, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14c15f240, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "mass", }, expected_py: None, name: "literal['mass']", }, ), }, ), ), required: true, }, "material_density": SerField { key_py: Py( 0x00007fa14bb4f870, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "output_unit": SerField { key_py: Py( 0x00007fa14bb4f470, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb036e0c50, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "OptionMass", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMass", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007fa14ade8df0, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007fa14ade8db0, ), ), ], ), }, name_py: Py( 0x00007fa14b038970, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "material_density", lookup_key: Simple { key: "material_density", py_key: Py( 0x00007fa14ade8e70, ), path: LookupPath( [ S( "material_density", Py( 0x00007fa14ade8e30, ), ), ], ), }, name_py: Py( 0x00007fa14bb4f870, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "material_density_unit", lookup_key: Simple { key: "material_density_unit", py_key: Py( 0x00007fa14ade8eb0, ), path: LookupPath( [ S( "material_density_unit", Py( 0x00007fa14ade8ef0, ), ), ], ), }, name_py: Py( 0x00007fa14bb4f8f0, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb036e0560, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "lb:ft3": 0, "kg:m3": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c7cb0, ), Py( 0x00007fa14b3c7d10, ), ], }, missing: None, expected_repr: "'lb:ft3' or 'kg:m3'", strict: false, class_repr: "UnitDensity", name: "str-enum[UnitDensity]", }, ), frozen: false, }, Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007fa14bd84070, ), path: LookupPath( [ S( "output_unit", Py( 0x00007fa14ade8f30, ), ), ], ), }, name_py: Py( 0x00007fa14bb4f470, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb036e0c50, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "g": 0, "kg": 1, "lb": 2, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c7dd0, ), Py( 0x00007fa14b3c7e30, ), Py( 0x00007fa14b3c7e90, ), ], }, missing: None, expected_repr: "'g', 'kg' or 'lb'", strict: false, class_repr: "UnitMass", name: "str-enum[UnitMass]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc6670, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc66d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14c15f240, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "mass": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14c15f240, ), ], }, expected_repr: "'mass'", name: "literal['mass']", }, ), validate_default: false, copy_default: false, name: "default[literal['mass']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionMass", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b78af0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionMass", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entity_ids: List[str], material_density: float, material_density_unit: kittycad.models.unit_density.UnitDensity, output_unit: kittycad.models.unit_mass.UnitMass, type: Literal['mass'] = 'mass') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
-
material_density_unit:
UnitDensity[source]
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entity_ids': FieldInfo(annotation=List[str], required=True), 'material_density': FieldInfo(annotation=float, required=True), 'material_density_unit': FieldInfo(annotation=UnitDensity, required=True), 'output_unit': FieldInfo(annotation=UnitMass, required=True), 'type': FieldInfo(annotation=Literal['mass'], required=False, default='mass')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionMouseClick(**data)[source][source]
Send a mouse click event Updates modified/selected entities.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['mouse_click'], 'window': <class 'kittycad.models.point2d.Point2d'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionMouseClick'>, 'config': {'title': 'OptionMouseClick'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionMouseClick'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionMouseClick'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionMouseClick:94467867651552', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'mouse_click', 'schema': {'expected': ['mouse_click'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'window': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'OptionMouseClick', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b1ade0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd79670, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "mouse_click", }, expected_py: None, name: "literal['mouse_click']", }, ), }, ), ), required: true, }, "window": SerField { key_py: Py( 0x00007fa14e1645a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionMouseClick", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMouseClick", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae74810, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae74750, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd79670, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "mouse_click": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd79670, ), ], }, expected_repr: "'mouse_click'", name: "literal['mouse_click']", }, ), validate_default: false, copy_default: false, name: "default[literal['mouse_click']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "window", lookup_key: Simple { key: "window", py_key: Py( 0x00007fa14ae762e0, ), path: LookupPath( [ S( "window", Py( 0x00007fa14ad542d0, ), ), ], ), }, name_py: Py( 0x00007fa14e1645a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ad53280, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ad53280, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), frozen: false, }, ], model_name: "OptionMouseClick", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b1ade0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionMouseClick", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['mouse_click'] = 'mouse_click', window: kittycad.models.point2d.Point2d) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['mouse_click'], required=False, default='mouse_click'), 'window': FieldInfo(annotation=Point2d, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionMouseMove(**data)[source][source]
Send a mouse move event
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'sequence': typing.Optional[int], 'type': typing.Literal['mouse_move'], 'window': <class 'kittycad.models.point2d.Point2d'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionMouseMove'>, 'config': {'title': 'OptionMouseMove'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionMouseMove'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionMouseMove'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionMouseMove:94467867642240', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'sequence': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'mouse_move', 'schema': {'expected': ['mouse_move'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'window': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'OptionMouseMove', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b18980, ), serializer: Fields( GeneralFieldsSerializer { fields: { "sequence": SerField { key_py: Py( 0x00007fa14fa3b790, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Int( IntSerializer, ), }, ), }, ), ), required: true, }, "window": SerField { key_py: Py( 0x00007fa14e1645a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd796f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "mouse_move", }, expected_py: None, name: "literal['mouse_move']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionMouseMove", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMouseMove", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "sequence", lookup_key: Simple { key: "sequence", py_key: Py( 0x00007fa14ad65870, ), path: LookupPath( [ S( "sequence", Py( 0x00007fa14ad65830, ), ), ], ), }, name_py: Py( 0x00007fa14fa3b790, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Int( IntValidator { strict: false, }, ), name: "nullable[int]", }, ), validate_default: false, copy_default: false, name: "default[nullable[int]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad54390, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad54450, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd796f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "mouse_move": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd796f0, ), ], }, expected_repr: "'mouse_move'", name: "literal['mouse_move']", }, ), validate_default: false, copy_default: false, name: "default[literal['mouse_move']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "window", lookup_key: Simple { key: "window", py_key: Py( 0x00007fa14ad54480, ), path: LookupPath( [ S( "window", Py( 0x00007fa14ad54420, ), ), ], ), }, name_py: Py( 0x00007fa14e1645a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ad65700, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ad65700, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), frozen: false, }, ], model_name: "OptionMouseMove", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b18980, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionMouseMove", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, sequence: Optional[int] = None, type: Literal['mouse_move'] = 'mouse_move', window: kittycad.models.point2d.Point2d) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'sequence': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['mouse_move'], required=False, default='mouse_move'), 'window': FieldInfo(annotation=Point2d, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionMovePathPen(**data)[source][source]
Move the path’s “pen”. If you’re in sketch mode, these coordinates are in the local coordinate system, not the world’s coordinate system. For example, say you’re sketching on the plane {x: (1,0,0), y: (0,1,0), origin: (0, 0, 50)}. In other words, the plane 50 units above the default XY plane. Then, moving the pen to (1, 1, 0) with this command uses local coordinates. So, it would move the pen to (1, 1, 50) in global coordinates.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'path': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'to': <class 'kittycad.models.point3d.Point3d'>, 'type': typing.Literal['move_path_pen']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionMovePathPen'>, 'config': {'title': 'OptionMovePathPen'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionMovePathPen'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionMovePathPen'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionMovePathPen:94467866442672', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'path': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'type': 'model-field'}, 'to': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'move_path_pen', 'schema': {'expected': ['move_path_pen'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMovePathPen', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb039f3bb0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "path": SerField { key_py: Py( 0x00007fa14fa3ac30, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd79770, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "move_path_pen", }, expected_py: None, name: "literal['move_path_pen']", }, ), }, ), ), required: true, }, "to": SerField { key_py: Py( 0x00007fa14eb8b120, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0330e750, ), serializer: Fields( GeneralFieldsSerializer { fields: { "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionMovePathPen", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMovePathPen", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "path", lookup_key: Simple { key: "path", py_key: Py( 0x00007fa14afe5140, ), path: LookupPath( [ S( "path", Py( 0x00007fa14afe5170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3ac30, ), validator: FunctionAfter( FunctionAfterValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), func: Py( 0x000055eb039991a0, ), config: Py( 0x00007fa14afe1a80, ), name: "function-after[ModelingCmdId(), str]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "to", lookup_key: Simple { key: "to", py_key: Py( 0x00007fa14afe51a0, ), path: LookupPath( [ S( "to", Py( 0x00007fa14afe51d0, ), ), ], ), }, name_py: Py( 0x00007fa14eb8b120, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0330e750, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point3d", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe5200, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe5230, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd79770, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "move_path_pen": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd79770, ), ], }, expected_repr: "'move_path_pen'", name: "literal['move_path_pen']", }, ), validate_default: false, copy_default: false, name: "default[literal['move_path_pen']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionMovePathPen", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039f3bb0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionMovePathPen", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, path: kittycad.models.modeling_cmd_id.ModelingCmdId, to: kittycad.models.point3d.Point3d, type: Literal['move_path_pen'] = 'move_path_pen') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'path': FieldInfo(annotation=ModelingCmdId, required=True), 'to': FieldInfo(annotation=Point3d, required=True), 'type': FieldInfo(annotation=Literal['move_path_pen'], required=False, default='move_path_pen')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
-
path:
ModelingCmdId[source]
- class kittycad.models.modeling_cmd.OptionNewAnnotation(**data)[source][source]
Create a new annotation
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'annotation_type': <enum 'AnnotationType'>, 'clobber': <class 'bool'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'options': <class 'kittycad.models.annotation_options.AnnotationOptions'>, 'type': typing.Literal['new_annotation']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <enum 'AnnotationLineEnd'>, 'members': [AnnotationLineEnd.NONE, AnnotationLineEnd.ARROW], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.annotation_line_end.AnnotationLineEnd:94467860848624', 'sub_type': 'str', 'type': 'enum'}], 'schema': {'cls': <class 'kittycad.models.modeling_cmd.OptionNewAnnotation'>, 'config': {'title': 'OptionNewAnnotation'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionNewAnnotation'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionNewAnnotation'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionNewAnnotation:94467867232592', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'annotation_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'AnnotationType'>, 'members': [AnnotationType.T2D, AnnotationType.T3D], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.annotation_type.AnnotationType:94467862345408', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'clobber': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'options': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.annotation_options.AnnotationOptions'>, 'config': {'title': 'AnnotationOptions'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.annotation_options.AnnotationOptions'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.annotation_options.AnnotationOptions'>>]}, 'ref': 'kittycad.models.annotation_options.AnnotationOptions:94467859218624', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'color': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.color.Color'>, 'config': {'title': 'Color'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.color.Color'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.color.Color'>>]}, 'ref': 'kittycad.models.color.Color:94467859422064', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'a': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'b': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'g': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'r': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Color', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'line_ends': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.annotation_line_end_options.AnnotationLineEndOptions'>, 'config': {'title': 'AnnotationLineEndOptions'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.annotation_line_end_options.AnnotationLineEndOptions'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.annotation_line_end_options.AnnotationLineEndOptions'>>]}, 'ref': 'kittycad.models.annotation_line_end_options.AnnotationLineEndOptions:94467862299104', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'end': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.annotation_line_end.AnnotationLineEnd:94467860848624', 'type': 'definition-ref'}, 'type': 'model-field'}, 'start': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.annotation_line_end.AnnotationLineEnd:94467860848624', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'AnnotationLineEndOptions', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'line_width': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'position': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'text': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.annotation_text_options.AnnotationTextOptions'>, 'config': {'title': 'AnnotationTextOptions'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.annotation_text_options.AnnotationTextOptions'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.annotation_text_options.AnnotationTextOptions'>>]}, 'ref': 'kittycad.models.annotation_text_options.AnnotationTextOptions:94467859412128', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'point_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}, 'text': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'AnnotationTextAlignmentX'>, 'members': [AnnotationTextAlignmentX.LEFT, AnnotationTextAlignmentX.CENTER, AnnotationTextAlignmentX.RIGHT], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.annotation_text_alignment_x.AnnotationTextAlignmentX:94467862308832', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'AnnotationTextAlignmentY'>, 'members': [AnnotationTextAlignmentY.BOTTOM, AnnotationTextAlignmentY.CENTER, AnnotationTextAlignmentY.TOP], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.annotation_text_alignment_y.AnnotationTextAlignmentY:94467859410352', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'AnnotationTextOptions', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'AnnotationOptions', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'new_annotation', 'schema': {'expected': ['new_annotation'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionNewAnnotation', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ab4950, ), serializer: Fields( GeneralFieldsSerializer { fields: { "options": SerField { key_py: Py( 0x00007fa14fa3aa38, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb033100c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "line_ends": SerField { key_py: Py( 0x00007fa14b1d3770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055eb036001e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "start": SerField { key_py: Py( 0x00007fa14fa3bce8, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: false, }, ), ), required: true, }, "end": SerField { key_py: Py( 0x00007fa14fa38368, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: false, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "AnnotationLineEndOptions", }, ), }, ), }, ), ), required: true, }, "position": SerField { key_py: Py( 0x00007fa14e255bf0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055eb0330e750, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), }, ), }, ), ), required: true, }, "color": SerField { key_py: Py( 0x00007fa14ea1a040, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055eb03341b70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "g": SerField { key_py: Py( 0x00007fa14fa3de10, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "r": SerField { key_py: Py( 0x00007fa14fa3e020, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "a": SerField { key_py: Py( 0x00007fa14fa3dcf0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "b": SerField { key_py: Py( 0x00007fa14fa3dd20, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Color", }, ), }, ), }, ), ), required: true, }, "text": SerField { key_py: Py( 0x00007fa14fa3c298, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055eb0333f4a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "text": SerField { key_py: Py( 0x00007fa14fa3c298, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb0333edb0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "point_size": SerField { key_py: Py( 0x00007fa14b2a67b0, ), alias: None, alias_py: None, serializer: Some( Int( IntSerializer, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb036027e0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "AnnotationTextOptions", }, ), }, ), }, ), ), required: true, }, "line_width": SerField { key_py: Py( 0x00007fa14b1d37f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "AnnotationOptions", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd797f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "new_annotation", }, expected_py: None, name: "literal['new_annotation']", }, ), }, ), ), required: true, }, "annotation_type": SerField { key_py: Py( 0x00007fa14e8602f0, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb0360b6c0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "clobber": SerField { key_py: Py( 0x00007fa14afe6e20, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "OptionNewAnnotation", }, ), definitions=[Enum(EnumSerializer { class: Py(0x55eb0349dff0), serializer: Some(Str(StrSerializer)) })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionNewAnnotation", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "annotation_type", lookup_key: Simple { key: "annotation_type", py_key: Py( 0x00007fa14aefdd30, ), path: LookupPath( [ S( "annotation_type", Py( 0x00007fa14aefdcf0, ), ), ], ), }, name_py: Py( 0x00007fa14e8602f0, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb0360b6c0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "t3d": 1, "t2d": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b362570, ), Py( 0x00007fa14b3625d0, ), ], }, missing: None, expected_repr: "'t2d' or 't3d'", strict: false, class_repr: "AnnotationType", name: "str-enum[AnnotationType]", }, ), frozen: false, }, Field { name: "clobber", lookup_key: Simple { key: "clobber", py_key: Py( 0x00007fa14b025c80, ), path: LookupPath( [ S( "clobber", Py( 0x00007fa14b025da0, ), ), ], ), }, name_py: Py( 0x00007fa14afe6e20, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "options", lookup_key: Simple { key: "options", py_key: Py( 0x00007fa14b024c30, ), path: LookupPath( [ S( "options", Py( 0x00007fa14b024a50, ), ), ], ), }, name_py: Py( 0x00007fa14fa3aa38, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "color", lookup_key: Simple { key: "color", py_key: Py( 0x00007fa14b025e30, ), path: LookupPath( [ S( "color", Py( 0x00007fa14b025bc0, ), ), ], ), }, name_py: Py( 0x00007fa14ea1a040, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "a", lookup_key: Simple { key: "a", py_key: Py( 0x00007fa14fa3dcf0, ), path: LookupPath( [ S( "a", Py( 0x00007fa14fa3dcf0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3dcf0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "b", lookup_key: Simple { key: "b", py_key: Py( 0x00007fa14fa3dd20, ), path: LookupPath( [ S( "b", Py( 0x00007fa14fa3dd20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3dd20, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "g", lookup_key: Simple { key: "g", py_key: Py( 0x00007fa14fa3de10, ), path: LookupPath( [ S( "g", Py( 0x00007fa14fa3de10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3de10, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "r", lookup_key: Simple { key: "r", py_key: Py( 0x00007fa14fa3e020, ), path: LookupPath( [ S( "r", Py( 0x00007fa14fa3e020, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e020, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Color", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03341b70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Color", }, ), name: "nullable[Color]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Color]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "line_ends", lookup_key: Simple { key: "line_ends", py_key: Py( 0x00007fa14aefddb0, ), path: LookupPath( [ S( "line_ends", Py( 0x00007fa14aefdd70, ), ), ], ), }, name_py: Py( 0x00007fa14b1d3770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "end", lookup_key: Simple { key: "end", py_key: Py( 0x00007fa14b025c20, ), path: LookupPath( [ S( "end", Py( 0x00007fa14b025ad0, ), ), ], ), }, name_py: Py( 0x00007fa14fa38368, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "start", lookup_key: Simple { key: "start", py_key: Py( 0x00007fa14b025e60, ), path: LookupPath( [ S( "start", Py( 0x00007fa14b025b00, ), ), ], ), }, name_py: Py( 0x00007fa14fa3bce8, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "AnnotationLineEndOptions", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb036001e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "AnnotationLineEndOptions", }, ), name: "nullable[AnnotationLineEndOptions]", }, ), validate_default: false, copy_default: false, name: "default[nullable[AnnotationLineEndOptions]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "line_width", lookup_key: Simple { key: "line_width", py_key: Py( 0x00007fa14aefddf0, ), path: LookupPath( [ S( "line_width", Py( 0x00007fa14aefde30, ), ), ], ), }, name_py: Py( 0x00007fa14b1d37f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "position", lookup_key: Simple { key: "position", py_key: Py( 0x00007fa14aefde70, ), path: LookupPath( [ S( "position", Py( 0x00007fa14aefdeb0, ), ), ], ), }, name_py: Py( 0x00007fa14e255bf0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0330e750, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point3d", }, ), name: "nullable[Point3d]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Point3d]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "text", lookup_key: Simple { key: "text", py_key: Py( 0x00007fa14b0c68e0, ), path: LookupPath( [ S( "text", Py( 0x00007fa14b0250b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c298, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "point_size", lookup_key: Simple { key: "point_size", py_key: Py( 0x00007fa14aefdef0, ), path: LookupPath( [ S( "point_size", Py( 0x00007fa14aefdf30, ), ), ], ), }, name_py: Py( 0x00007fa14b2a67b0, ), validator: Int( IntValidator { strict: false, }, ), frozen: false, }, Field { name: "text", lookup_key: Simple { key: "text", py_key: Py( 0x00007fa14b024a80, ), path: LookupPath( [ S( "text", Py( 0x00007fa14b024c90, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c298, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb036027e0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "center": 1, "right": 2, "left": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b361f10, ), Py( 0x00007fa14b361f70, ), Py( 0x00007fa14b361fd0, ), ], }, missing: None, expected_repr: "'left', 'center' or 'right'", strict: false, class_repr: "AnnotationTextAlignmentX", name: "str-enum[AnnotationTextAlignmentX]", }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb0333edb0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "center": 1, "top": 2, "bottom": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3620f0, ), Py( 0x00007fa14b362150, ), Py( 0x00007fa14b3621b0, ), ], }, missing: None, expected_repr: "'bottom', 'center' or 'top'", strict: false, class_repr: "AnnotationTextAlignmentY", name: "str-enum[AnnotationTextAlignmentY]", }, ), frozen: false, }, ], model_name: "AnnotationTextOptions", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0333f4a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "AnnotationTextOptions", }, ), name: "nullable[AnnotationTextOptions]", }, ), validate_default: false, copy_default: false, name: "default[nullable[AnnotationTextOptions]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "AnnotationOptions", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb033100c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "AnnotationOptions", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b024e10, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b024bd0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd797f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "new_annotation": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd797f0, ), ], }, expected_repr: "'new_annotation'", name: "literal['new_annotation']", }, ), validate_default: false, copy_default: false, name: "default[literal['new_annotation']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionNewAnnotation", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ab4950, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionNewAnnotation", }, ), definitions=[StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x55eb0349dff0), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"arrow": 1, "none": 0}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7fa14b361730), Py(0x7fa14b361c10)] }, missing: None, expected_repr: "'none' or 'arrow'", strict: false, class_repr: "AnnotationLineEnd", name: "str-enum[AnnotationLineEnd]" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, annotation_type: kittycad.models.annotation_type.AnnotationType, clobber: bool, options: kittycad.models.annotation_options.AnnotationOptions, type: Literal['new_annotation'] = 'new_annotation') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
-
annotation_type:
AnnotationType[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'annotation_type': FieldInfo(annotation=AnnotationType, required=True), 'clobber': FieldInfo(annotation=bool, required=True), 'options': FieldInfo(annotation=AnnotationOptions, required=True), 'type': FieldInfo(annotation=Literal['new_annotation'], required=False, default='new_annotation')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
-
options:
AnnotationOptions[source]
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionObjectBringToFront(**data)[source][source]
Bring an object to the front of the scene
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['object_bring_to_front']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionObjectBringToFront'>, 'config': {'title': 'OptionObjectBringToFront'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionObjectBringToFront'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionObjectBringToFront'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionObjectBringToFront:94467867362448', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'object_bring_to_front', 'schema': {'expected': ['object_bring_to_front'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionObjectBringToFront', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ad4490, ), serializer: Fields( GeneralFieldsSerializer { fields: { "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd79a70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "object_bring_to_front", }, expected_py: None, name: "literal['object_bring_to_front']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionObjectBringToFront", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionObjectBringToFront", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14aeff370, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14aefecb0, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b025fb0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b026040, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd79a70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "object_bring_to_front": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd79a70, ), ], }, expected_repr: "'object_bring_to_front'", name: "literal['object_bring_to_front']", }, ), validate_default: false, copy_default: false, name: "default[literal['object_bring_to_front']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionObjectBringToFront", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ad4490, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionObjectBringToFront", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, object_id: str, type: Literal['object_bring_to_front'] = 'object_bring_to_front') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['object_bring_to_front'], required=False, default='object_bring_to_front')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionObjectSetMaterialParamsPbr(**data)[source][source]
Set the material properties of an object
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'ambient_occlusion': <class 'float'>, 'color': <class 'kittycad.models.color.Color'>, 'metalness': <class 'float'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'roughness': <class 'float'>, 'type': typing.Literal['object_set_material_params_pbr']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionObjectSetMaterialParamsPbr'>, 'config': {'title': 'OptionObjectSetMaterialParamsPbr'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionObjectSetMaterialParamsPbr'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionObjectSetMaterialParamsPbr'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionObjectSetMaterialParamsPbr:94467867373424', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'ambient_occlusion': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'color': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.color.Color'>, 'config': {'title': 'Color'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.color.Color'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.color.Color'>>]}, 'ref': 'kittycad.models.color.Color:94467859422064', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'a': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'b': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'g': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'r': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Color', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'metalness': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'roughness': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'object_set_material_params_pbr', 'schema': {'expected': ['object_set_material_params_pbr'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionObjectSetMaterialParamsPbr', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ad6f70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd29ac0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "object_set_material_params_pbr", }, expected_py: None, name: "literal['object_set_material_params_pbr']", }, ), }, ), ), required: true, }, "ambient_occlusion": SerField { key_py: Py( 0x00007fa14affc430, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "metalness": SerField { key_py: Py( 0x00007fa14affc470, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "color": SerField { key_py: Py( 0x00007fa14ea1a040, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03341b70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "a": SerField { key_py: Py( 0x00007fa14fa3dcf0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "b": SerField { key_py: Py( 0x00007fa14fa3dd20, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "g": SerField { key_py: Py( 0x00007fa14fa3de10, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "r": SerField { key_py: Py( 0x00007fa14fa3e020, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Color", }, ), ), required: true, }, "roughness": SerField { key_py: Py( 0x00007fa14affc4f0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 6, }, ), has_extra: false, root_model: false, name: "OptionObjectSetMaterialParamsPbr", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionObjectSetMaterialParamsPbr", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "ambient_occlusion", lookup_key: Simple { key: "ambient_occlusion", py_key: Py( 0x00007fa14af07730, ), path: LookupPath( [ S( "ambient_occlusion", Py( 0x00007fa14af076f0, ), ), ], ), }, name_py: Py( 0x00007fa14affc430, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "color", lookup_key: Simple { key: "color", py_key: Py( 0x00007fa14b0271e0, ), path: LookupPath( [ S( "color", Py( 0x00007fa14b027210, ), ), ], ), }, name_py: Py( 0x00007fa14ea1a040, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "a", lookup_key: Simple { key: "a", py_key: Py( 0x00007fa14fa3dcf0, ), path: LookupPath( [ S( "a", Py( 0x00007fa14fa3dcf0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3dcf0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "b", lookup_key: Simple { key: "b", py_key: Py( 0x00007fa14fa3dd20, ), path: LookupPath( [ S( "b", Py( 0x00007fa14fa3dd20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3dd20, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "g", lookup_key: Simple { key: "g", py_key: Py( 0x00007fa14fa3de10, ), path: LookupPath( [ S( "g", Py( 0x00007fa14fa3de10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3de10, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "r", lookup_key: Simple { key: "r", py_key: Py( 0x00007fa14fa3e020, ), path: LookupPath( [ S( "r", Py( 0x00007fa14fa3e020, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e020, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Color", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03341b70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Color", }, ), frozen: false, }, Field { name: "metalness", lookup_key: Simple { key: "metalness", py_key: Py( 0x00007fa14af077b0, ), path: LookupPath( [ S( "metalness", Py( 0x00007fa14af07770, ), ), ], ), }, name_py: Py( 0x00007fa14affc470, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14af077f0, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14af07830, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "roughness", lookup_key: Simple { key: "roughness", py_key: Py( 0x00007fa14af07870, ), path: LookupPath( [ S( "roughness", Py( 0x00007fa14af078b0, ), ), ], ), }, name_py: Py( 0x00007fa14affc4f0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b027240, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b027270, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd29ac0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "object_set_material_params_pbr": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd29ac0, ), ], }, expected_repr: "'object_set_material_params_pbr'", name: "literal['object_set_material_params_pbr']", }, ), validate_default: false, copy_default: false, name: "default[literal['object_set_material_params_pbr']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionObjectSetMaterialParamsPbr", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ad6f70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionObjectSetMaterialParamsPbr", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, ambient_occlusion: float, color: kittycad.models.color.Color, metalness: float, object_id: str, roughness: float, type: Literal['object_set_material_params_pbr'] = 'object_set_material_params_pbr') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'ambient_occlusion': FieldInfo(annotation=float, required=True), 'color': FieldInfo(annotation=Color, required=True), 'metalness': FieldInfo(annotation=float, required=True), 'object_id': FieldInfo(annotation=str, required=True), 'roughness': FieldInfo(annotation=float, required=True), 'type': FieldInfo(annotation=Literal['object_set_material_params_pbr'], required=False, default='object_set_material_params_pbr')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionObjectVisible(**data)[source][source]
Hide or show an object
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'hidden': <class 'bool'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['object_visible']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionObjectVisible'>, 'config': {'title': 'OptionObjectVisible'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionObjectVisible'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionObjectVisible'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionObjectVisible:94467866430624', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'hidden': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'object_visible', 'schema': {'expected': ['object_visible'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionObjectVisible', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb039f0ca0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd79bb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "object_visible", }, expected_py: None, name: "literal['object_visible']", }, ), }, ), ), required: true, }, "hidden": SerField { key_py: Py( 0x00007fa14e0bbea0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionObjectVisible", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionObjectVisible", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "hidden", lookup_key: Simple { key: "hidden", py_key: Py( 0x00007fa14b026e20, ), path: LookupPath( [ S( "hidden", Py( 0x00007fa14b026e50, ), ), ], ), }, name_py: Py( 0x00007fa14e0bbea0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14af06ef0, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14af06eb0, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b026e80, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b026eb0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd79bb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "object_visible": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd79bb0, ), ], }, expected_repr: "'object_visible'", name: "literal['object_visible']", }, ), validate_default: false, copy_default: false, name: "default[literal['object_visible']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionObjectVisible", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039f0ca0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionObjectVisible", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, hidden: bool, object_id: str, type: Literal['object_visible'] = 'object_visible') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'hidden': FieldInfo(annotation=bool, required=True), 'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['object_visible'], required=False, default='object_visible')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionPathGetCurveUuid(**data)[source][source]
Obtain curve id by index
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'index': <class 'int'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'path_id': <class 'str'>, 'type': typing.Literal['path_get_curve_uuid']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionPathGetCurveUuid'>, 'config': {'title': 'OptionPathGetCurveUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionPathGetCurveUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionPathGetCurveUuid'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionPathGetCurveUuid:94467856150624', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'index': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}, 'path_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_curve_uuid', 'schema': {'expected': ['path_get_curve_uuid'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetCurveUuid', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03023060, ), serializer: Fields( GeneralFieldsSerializer { fields: { "index": SerField { key_py: Py( 0x00007fa14f9faac0, ), alias: None, alias_py: None, serializer: Some( Int( IntSerializer, ), ), required: true, }, "path_id": SerField { key_py: Py( 0x00007fa14afd3120, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7a430, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_curve_uuid", }, expected_py: None, name: "literal['path_get_curve_uuid']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionPathGetCurveUuid", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetCurveUuid", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "index", lookup_key: Simple { key: "index", py_key: Py( 0x00007fa14ad57210, ), path: LookupPath( [ S( "index", Py( 0x00007fa14ad57240, ), ), ], ), }, name_py: Py( 0x00007fa14f9faac0, ), validator: Int( IntValidator { strict: false, }, ), frozen: false, }, Field { name: "path_id", lookup_key: Simple { key: "path_id", py_key: Py( 0x00007fa14ad57270, ), path: LookupPath( [ S( "path_id", Py( 0x00007fa14ad572a0, ), ), ], ), }, name_py: Py( 0x00007fa14afd3120, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad572d0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad57300, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7a430, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_curve_uuid": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7a430, ), ], }, expected_repr: "'path_get_curve_uuid'", name: "literal['path_get_curve_uuid']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_curve_uuid']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionPathGetCurveUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03023060, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPathGetCurveUuid", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, index: int, path_id: str, type: Literal['path_get_curve_uuid'] = 'path_get_curve_uuid') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'index': FieldInfo(annotation=int, required=True), 'path_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['path_get_curve_uuid'], required=False, default='path_get_curve_uuid')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionPathGetCurveUuidsForVertices(**data)[source][source]
Obtain curve ids for vertex ids
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'path_id': <class 'str'>, 'type': typing.Literal['path_get_curve_uuids_for_vertices'], 'vertex_ids': typing.List[str]}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionPathGetCurveUuidsForVertices'>, 'config': {'title': 'OptionPathGetCurveUuidsForVertices'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionPathGetCurveUuidsForVertices'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionPathGetCurveUuidsForVertices'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionPathGetCurveUuidsForVertices:94467867827808', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'path_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_curve_uuids_for_vertices', 'schema': {'expected': ['path_get_curve_uuids_for_vertices'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'vertex_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetCurveUuidsForVertices', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b45e60, ), serializer: Fields( GeneralFieldsSerializer { fields: { "vertex_ids": SerField { key_py: Py( 0x00007fa14b01fc70, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "path_id": SerField { key_py: Py( 0x00007fa14afd3120, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd297f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_curve_uuids_for_vertices", }, expected_py: None, name: "literal['path_get_curve_uuids_for_vertices']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionPathGetCurveUuidsForVertices", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetCurveUuidsForVertices", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "path_id", lookup_key: Simple { key: "path_id", py_key: Py( 0x00007fa14ad55a10, ), path: LookupPath( [ S( "path_id", Py( 0x00007fa14ad54510, ), ), ], ), }, name_py: Py( 0x00007fa14afd3120, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad55c50, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad55e00, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd297f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_curve_uuids_for_vertices": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd297f0, ), ], }, expected_repr: "'path_get_curve_uuids_for_vertices'", name: "literal['path_get_curve_uuids_for_vertices']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_curve_uuids_for_vertices']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "vertex_ids", lookup_key: Simple { key: "vertex_ids", py_key: Py( 0x00007fa14adb8370, ), path: LookupPath( [ S( "vertex_ids", Py( 0x00007fa14adb8270, ), ), ], ), }, name_py: Py( 0x00007fa14b01fc70, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "OptionPathGetCurveUuidsForVertices", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b45e60, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPathGetCurveUuidsForVertices", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, path_id: str, type: Literal['path_get_curve_uuids_for_vertices'] = 'path_get_curve_uuids_for_vertices', vertex_ids: List[str]) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'path_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['path_get_curve_uuids_for_vertices'], required=False, default='path_get_curve_uuids_for_vertices'), 'vertex_ids': FieldInfo(annotation=List[str], required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionPathGetInfo(**data)[source][source]
Query the given path.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'path_id': <class 'str'>, 'type': typing.Literal['path_get_info']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionPathGetInfo'>, 'config': {'title': 'OptionPathGetInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionPathGetInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionPathGetInfo'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionPathGetInfo:94467867818416', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'path_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_info', 'schema': {'expected': ['path_get_info'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetInfo', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b439b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7a530, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_info", }, expected_py: None, name: "literal['path_get_info']", }, ), }, ), ), required: true, }, "path_id": SerField { key_py: Py( 0x00007fa14afd3120, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathGetInfo", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetInfo", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "path_id", lookup_key: Simple { key: "path_id", py_key: Py( 0x00007fa14ad56dc0, ), path: LookupPath( [ S( "path_id", Py( 0x00007fa14ad56df0, ), ), ], ), }, name_py: Py( 0x00007fa14afd3120, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad56e20, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad56e50, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7a530, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_info": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7a530, ), ], }, expected_repr: "'path_get_info'", name: "literal['path_get_info']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_info']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionPathGetInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b439b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPathGetInfo", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, path_id: str, type: Literal['path_get_info'] = 'path_get_info') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'path_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['path_get_info'], required=False, default='path_get_info')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionPathGetSketchTargetUuid(**data)[source][source]
Obtain the sketch target id (if the path was drawn in sketchmode) for a path
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'path_id': <class 'str'>, 'type': typing.Literal['path_get_sketch_target_uuid']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionPathGetSketchTargetUuid'>, 'config': {'title': 'OptionPathGetSketchTargetUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionPathGetSketchTargetUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionPathGetSketchTargetUuid'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionPathGetSketchTargetUuid:94467867853312', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'path_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_sketch_target_uuid', 'schema': {'expected': ['path_get_sketch_target_uuid'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetSketchTargetUuid', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b4c200, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2add0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_sketch_target_uuid", }, expected_py: None, name: "literal['path_get_sketch_target_uuid']", }, ), }, ), ), required: true, }, "path_id": SerField { key_py: Py( 0x00007fa14afd3120, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathGetSketchTargetUuid", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetSketchTargetUuid", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "path_id", lookup_key: Simple { key: "path_id", py_key: Py( 0x00007fa14ad57db0, ), path: LookupPath( [ S( "path_id", Py( 0x00007fa14ad57de0, ), ), ], ), }, name_py: Py( 0x00007fa14afd3120, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad57e10, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad57e40, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2add0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_sketch_target_uuid": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2add0, ), ], }, expected_repr: "'path_get_sketch_target_uuid'", name: "literal['path_get_sketch_target_uuid']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_sketch_target_uuid']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionPathGetSketchTargetUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b4c200, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPathGetSketchTargetUuid", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, path_id: str, type: Literal['path_get_sketch_target_uuid'] = 'path_get_sketch_target_uuid') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'path_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['path_get_sketch_target_uuid'], required=False, default='path_get_sketch_target_uuid')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionPathGetVertexUuids(**data)[source][source]
Obtain vertex ids for a path
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'path_id': <class 'str'>, 'type': typing.Literal['path_get_vertex_uuids']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionPathGetVertexUuids'>, 'config': {'title': 'OptionPathGetVertexUuids'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionPathGetVertexUuids'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionPathGetVertexUuids'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionPathGetVertexUuids:94467867835536', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'path_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_vertex_uuids', 'schema': {'expected': ['path_get_vertex_uuids'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetVertexUuids', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b47c90, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7a6b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_vertex_uuids", }, expected_py: None, name: "literal['path_get_vertex_uuids']", }, ), }, ), ), required: true, }, "path_id": SerField { key_py: Py( 0x00007fa14afd3120, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathGetVertexUuids", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetVertexUuids", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "path_id", lookup_key: Simple { key: "path_id", py_key: Py( 0x00007fa14ad57810, ), path: LookupPath( [ S( "path_id", Py( 0x00007fa14ad57840, ), ), ], ), }, name_py: Py( 0x00007fa14afd3120, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad57870, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad578a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7a6b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_vertex_uuids": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7a6b0, ), ], }, expected_repr: "'path_get_vertex_uuids'", name: "literal['path_get_vertex_uuids']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_vertex_uuids']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionPathGetVertexUuids", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b47c90, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPathGetVertexUuids", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, path_id: str, type: Literal['path_get_vertex_uuids'] = 'path_get_vertex_uuids') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'path_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['path_get_vertex_uuids'], required=False, default='path_get_vertex_uuids')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionPlaneIntersectAndProject(**data)[source][source]
Utility method. Performs both a ray cast and projection to plane-local coordinates. Returns the plane coordinates for the given window coordinates.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'plane_id': <class 'str'>, 'type': typing.Literal['plane_intersect_and_project'], 'window': <class 'kittycad.models.point2d.Point2d'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionPlaneIntersectAndProject'>, 'config': {'title': 'OptionPlaneIntersectAndProject'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionPlaneIntersectAndProject'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionPlaneIntersectAndProject'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionPlaneIntersectAndProject:94467867914096', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'plane_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'plane_intersect_and_project', 'schema': {'expected': ['plane_intersect_and_project'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'window': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'OptionPlaneIntersectAndProject', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b5af70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "window": SerField { key_py: Py( 0x00007fa14e1645a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), ), required: true, }, "plane_id": SerField { key_py: Py( 0x00007fa14b01dfb0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2af10, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "plane_intersect_and_project", }, expected_py: None, name: "literal['plane_intersect_and_project']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionPlaneIntersectAndProject", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPlaneIntersectAndProject", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "plane_id", lookup_key: Simple { key: "plane_id", py_key: Py( 0x00007fa14add04b0, ), path: LookupPath( [ S( "plane_id", Py( 0x00007fa14add0470, ), ), ], ), }, name_py: Py( 0x00007fa14b01dfb0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc4ab0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc4ae0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2af10, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "plane_intersect_and_project": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2af10, ), ], }, expected_repr: "'plane_intersect_and_project'", name: "literal['plane_intersect_and_project']", }, ), validate_default: false, copy_default: false, name: "default[literal['plane_intersect_and_project']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "window", lookup_key: Simple { key: "window", py_key: Py( 0x00007fa14adc4b10, ), path: LookupPath( [ S( "window", Py( 0x00007fa14adc4b40, ), ), ], ), }, name_py: Py( 0x00007fa14e1645a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14add0340, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14add0340, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), frozen: false, }, ], model_name: "OptionPlaneIntersectAndProject", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b5af70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPlaneIntersectAndProject", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, plane_id: str, type: Literal['plane_intersect_and_project'] = 'plane_intersect_and_project', window: kittycad.models.point2d.Point2d) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'plane_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['plane_intersect_and_project'], required=False, default='plane_intersect_and_project'), 'window': FieldInfo(annotation=Point2d, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionPlaneSetColor(**data)[source][source]
Set the color of a plane.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'color': <class 'kittycad.models.color.Color'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'plane_id': <class 'str'>, 'type': typing.Literal['plane_set_color']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionPlaneSetColor'>, 'config': {'title': 'OptionPlaneSetColor'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionPlaneSetColor'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionPlaneSetColor'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionPlaneSetColor:94467867603808', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'color': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.color.Color'>, 'config': {'title': 'Color'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.color.Color'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.color.Color'>>]}, 'ref': 'kittycad.models.color.Color:94467859422064', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'a': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'b': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'g': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'r': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Color', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'plane_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'plane_set_color', 'schema': {'expected': ['plane_set_color'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPlaneSetColor', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b0f360, ), serializer: Fields( GeneralFieldsSerializer { fields: { "plane_id": SerField { key_py: Py( 0x00007fa14b01dfb0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7acf0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "plane_set_color", }, expected_py: None, name: "literal['plane_set_color']", }, ), }, ), ), required: true, }, "color": SerField { key_py: Py( 0x00007fa14ea1a040, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03341b70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "r": SerField { key_py: Py( 0x00007fa14fa3e020, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "b": SerField { key_py: Py( 0x00007fa14fa3dd20, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "g": SerField { key_py: Py( 0x00007fa14fa3de10, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "a": SerField { key_py: Py( 0x00007fa14fa3dcf0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Color", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionPlaneSetColor", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPlaneSetColor", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "color", lookup_key: Simple { key: "color", py_key: Py( 0x00007fa14ad541b0, ), path: LookupPath( [ S( "color", Py( 0x00007fa14ad541e0, ), ), ], ), }, name_py: Py( 0x00007fa14ea1a040, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "a", lookup_key: Simple { key: "a", py_key: Py( 0x00007fa14fa3dcf0, ), path: LookupPath( [ S( "a", Py( 0x00007fa14fa3dcf0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3dcf0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "b", lookup_key: Simple { key: "b", py_key: Py( 0x00007fa14fa3dd20, ), path: LookupPath( [ S( "b", Py( 0x00007fa14fa3dd20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3dd20, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "g", lookup_key: Simple { key: "g", py_key: Py( 0x00007fa14fa3de10, ), path: LookupPath( [ S( "g", Py( 0x00007fa14fa3de10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3de10, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "r", lookup_key: Simple { key: "r", py_key: Py( 0x00007fa14fa3e020, ), path: LookupPath( [ S( "r", Py( 0x00007fa14fa3e020, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e020, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Color", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03341b70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Color", }, ), frozen: false, }, Field { name: "plane_id", lookup_key: Simple { key: "plane_id", py_key: Py( 0x00007fa14ad527b0, ), path: LookupPath( [ S( "plane_id", Py( 0x00007fa14ad52770, ), ), ], ), }, name_py: Py( 0x00007fa14b01dfb0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad54210, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad54240, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7acf0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "plane_set_color": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7acf0, ), ], }, expected_repr: "'plane_set_color'", name: "literal['plane_set_color']", }, ), validate_default: false, copy_default: false, name: "default[literal['plane_set_color']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionPlaneSetColor", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b0f360, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPlaneSetColor", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, color: kittycad.models.color.Color, plane_id: str, type: Literal['plane_set_color'] = 'plane_set_color') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'color': FieldInfo(annotation=Color, required=True), 'plane_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['plane_set_color'], required=False, default='plane_set_color')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionReconfigureStream(**data)[source][source]
Reconfigure the stream.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'bitrate': typing.Optional[int], 'fps': <class 'int'>, 'height': <class 'int'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['reconfigure_stream'], 'width': <class 'int'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionReconfigureStream'>, 'config': {'title': 'OptionReconfigureStream'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionReconfigureStream'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionReconfigureStream'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionReconfigureStream:94467867937760', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'bitrate': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'fps': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}, 'height': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'reconfigure_stream', 'schema': {'expected': ['reconfigure_stream'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'width': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}}, 'model_name': 'OptionReconfigureStream', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b60be0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "bitrate": SerField { key_py: Py( 0x00007fa14b0252f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Int( IntSerializer, ), }, ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7afb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "reconfigure_stream", }, expected_py: None, name: "literal['reconfigure_stream']", }, ), }, ), ), required: true, }, "fps": SerField { key_py: Py( 0x00007fa14b0253e0, ), alias: None, alias_py: None, serializer: Some( Int( IntSerializer, ), ), required: true, }, "height": SerField { key_py: Py( 0x00007fa14e3a6a30, ), alias: None, alias_py: None, serializer: Some( Int( IntSerializer, ), ), required: true, }, "width": SerField { key_py: Py( 0x00007fa14ef0b1b0, ), alias: None, alias_py: None, serializer: Some( Int( IntSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "OptionReconfigureStream", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionReconfigureStream", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "bitrate", lookup_key: Simple { key: "bitrate", py_key: Py( 0x00007fa14adc56e0, ), path: LookupPath( [ S( "bitrate", Py( 0x00007fa14adc5710, ), ), ], ), }, name_py: Py( 0x00007fa14b0252f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Int( IntValidator { strict: false, }, ), name: "nullable[int]", }, ), validate_default: false, copy_default: false, name: "default[nullable[int]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "fps", lookup_key: Simple { key: "fps", py_key: Py( 0x00007fa14adc5740, ), path: LookupPath( [ S( "fps", Py( 0x00007fa14adc5770, ), ), ], ), }, name_py: Py( 0x00007fa14b0253e0, ), validator: Int( IntValidator { strict: false, }, ), frozen: false, }, Field { name: "height", lookup_key: Simple { key: "height", py_key: Py( 0x00007fa14adc57a0, ), path: LookupPath( [ S( "height", Py( 0x00007fa14adc57d0, ), ), ], ), }, name_py: Py( 0x00007fa14e3a6a30, ), validator: Int( IntValidator { strict: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc5800, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc5830, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7afb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "reconfigure_stream": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7afb0, ), ], }, expected_repr: "'reconfigure_stream'", name: "literal['reconfigure_stream']", }, ), validate_default: false, copy_default: false, name: "default[literal['reconfigure_stream']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "width", lookup_key: Simple { key: "width", py_key: Py( 0x00007fa14adc5860, ), path: LookupPath( [ S( "width", Py( 0x00007fa14adc5890, ), ), ], ), }, name_py: Py( 0x00007fa14ef0b1b0, ), validator: Int( IntValidator { strict: false, }, ), frozen: false, }, ], model_name: "OptionReconfigureStream", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b60be0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionReconfigureStream", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, bitrate: Optional[int] = None, fps: int, height: int, type: Literal['reconfigure_stream'] = 'reconfigure_stream', width: int) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'bitrate': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'fps': FieldInfo(annotation=int, required=True), 'height': FieldInfo(annotation=int, required=True), 'type': FieldInfo(annotation=Literal['reconfigure_stream'], required=False, default='reconfigure_stream'), 'width': FieldInfo(annotation=int, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionRemoveSceneObjects(**data)[source][source]
Remove scene objects.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_ids': typing.List[str], 'type': typing.Literal['remove_scene_objects']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionRemoveSceneObjects'>, 'config': {'title': 'OptionRemoveSceneObjects'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionRemoveSceneObjects'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionRemoveSceneObjects'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionRemoveSceneObjects:94467867900416', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'object_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'remove_scene_objects', 'schema': {'expected': ['remove_scene_objects'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionRemoveSceneObjects', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b57a00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "object_ids": SerField { key_py: Py( 0x00007fa14ae2c770, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7b0b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "remove_scene_objects", }, expected_py: None, name: "literal['remove_scene_objects']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionRemoveSceneObjects", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionRemoveSceneObjects", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "object_ids", lookup_key: Simple { key: "object_ids", py_key: Py( 0x00007fa14adcaaf0, ), path: LookupPath( [ S( "object_ids", Py( 0x00007fa14adcaab0, ), ), ], ), }, name_py: Py( 0x00007fa14ae2c770, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc4750, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc4690, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7b0b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "remove_scene_objects": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7b0b0, ), ], }, expected_repr: "'remove_scene_objects'", name: "literal['remove_scene_objects']", }, ), validate_default: false, copy_default: false, name: "default[literal['remove_scene_objects']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionRemoveSceneObjects", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b57a00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionRemoveSceneObjects", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, object_ids: List[str], type: Literal['remove_scene_objects'] = 'remove_scene_objects') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'object_ids': FieldInfo(annotation=List[str], required=True), 'type': FieldInfo(annotation=Literal['remove_scene_objects'], required=False, default='remove_scene_objects')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionRevolve(**data)[source][source]
Command for revolving a solid 2d.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'angle': <class 'kittycad.models.angle.Angle'>, 'axis': <class 'kittycad.models.point3d.Point3d'>, 'axis_is_2d': <class 'bool'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'origin': <class 'kittycad.models.point3d.Point3d'>, 'target': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'tolerance': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': typing.Literal['revolve']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.modeling_cmd.OptionRevolve'>, 'config': {'title': 'OptionRevolve'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionRevolve'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionRevolve'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionRevolve:94467866550528', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'angle': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.angle.Angle'>, 'config': {'title': 'Angle'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.angle.Angle'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.angle.Angle'>>]}, 'ref': 'kittycad.models.angle.Angle:94467862185984', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitAngle'>, 'members': [UnitAngle.DEGREES, UnitAngle.RADIANS], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_angle.UnitAngle:94467859501968', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'value': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Angle', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'axis_is_2d': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'origin': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'target': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'type': 'model-field'}, 'tolerance': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'revolve', 'schema': {'expected': ['revolve'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionRevolve', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a0e100, ), serializer: Fields( GeneralFieldsSerializer { fields: { "tolerance": SerField { key_py: Py( 0x00007fa14afd8570, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14c398f00, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "revolve", }, expected_py: None, name: "literal['revolve']", }, ), }, ), ), required: true, }, "target": SerField { key_py: Py( 0x00007fa14fa3c088, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "origin": SerField { key_py: Py( 0x00007fa14fa3aa98, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "angle": SerField { key_py: Py( 0x00007fa14d9ed140, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb035e4800, ), serializer: Fields( GeneralFieldsSerializer { fields: { "unit": SerField { key_py: Py( 0x00007fa14e161380, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb03355390, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "value": SerField { key_py: Py( 0x00007fa14fa3c768, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Angle", }, ), ), required: true, }, "axis": SerField { key_py: Py( 0x00007fa14c13dce0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "axis_is_2d": SerField { key_py: Py( 0x00007fa14afd84f0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "OptionRevolve", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionRevolve", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "angle", lookup_key: Simple { key: "angle", py_key: Py( 0x00007fa14afe62b0, ), path: LookupPath( [ S( "angle", Py( 0x00007fa14afe62e0, ), ), ], ), }, name_py: Py( 0x00007fa14d9ed140, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "unit", lookup_key: Simple { key: "unit", py_key: Py( 0x00007fa14afe61f0, ), path: LookupPath( [ S( "unit", Py( 0x00007fa14afe6220, ), ), ], ), }, name_py: Py( 0x00007fa14e161380, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb03355390, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "degrees": 0, "radians": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b361850, ), Py( 0x00007fa14b3618b0, ), ], }, missing: None, expected_repr: "'degrees' or 'radians'", strict: false, class_repr: "UnitAngle", name: "str-enum[UnitAngle]", }, ), frozen: false, }, Field { name: "value", lookup_key: Simple { key: "value", py_key: Py( 0x00007fa14afe6250, ), path: LookupPath( [ S( "value", Py( 0x00007fa14afe6280, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c768, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Angle", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb035e4800, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Angle", }, ), frozen: false, }, Field { name: "axis", lookup_key: Simple { key: "axis", py_key: Py( 0x00007fa14afe6310, ), path: LookupPath( [ S( "axis", Py( 0x00007fa14afe6340, ), ), ], ), }, name_py: Py( 0x00007fa14c13dce0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "axis_is_2d", lookup_key: Simple { key: "axis_is_2d", py_key: Py( 0x00007fa14ae43f70, ), path: LookupPath( [ S( "axis_is_2d", Py( 0x00007fa14ae43fb0, ), ), ], ), }, name_py: Py( 0x00007fa14afd84f0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "origin", lookup_key: Simple { key: "origin", py_key: Py( 0x00007fa14afe6370, ), path: LookupPath( [ S( "origin", Py( 0x00007fa14afe63a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3aa98, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "target", lookup_key: Simple { key: "target", py_key: Py( 0x00007fa14afe63d0, ), path: LookupPath( [ S( "target", Py( 0x00007fa14afe6400, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c088, ), validator: FunctionAfter( FunctionAfterValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), func: Py( 0x000055eb039991a0, ), config: Py( 0x00007fa14ae71100, ), name: "function-after[ModelingCmdId(), str]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "tolerance", lookup_key: Simple { key: "tolerance", py_key: Py( 0x00007fa14ae410b0, ), path: LookupPath( [ S( "tolerance", Py( 0x00007fa14ae410f0, ), ), ], ), }, name_py: Py( 0x00007fa14afd8570, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae71100, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe6430, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe6460, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14c398f00, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "revolve": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14c398f00, ), ], }, expected_repr: "'revolve'", name: "literal['revolve']", }, ), validate_default: false, copy_default: false, name: "default[literal['revolve']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionRevolve", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a0e100, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionRevolve", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, angle: kittycad.models.angle.Angle, axis: kittycad.models.point3d.Point3d, axis_is_2d: bool, origin: kittycad.models.point3d.Point3d, target: kittycad.models.modeling_cmd_id.ModelingCmdId, tolerance: kittycad.models.length_unit.LengthUnit, type: Literal['revolve'] = 'revolve') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'angle': FieldInfo(annotation=Angle, required=True), 'axis': FieldInfo(annotation=Point3d, required=True), 'axis_is_2d': FieldInfo(annotation=bool, required=True), 'origin': FieldInfo(annotation=Point3d, required=True), 'target': FieldInfo(annotation=ModelingCmdId, required=True), 'tolerance': FieldInfo(annotation=LengthUnit, required=True), 'type': FieldInfo(annotation=Literal['revolve'], required=False, default='revolve')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
target:
ModelingCmdId[source]
-
tolerance:
LengthUnit[source]
- class kittycad.models.modeling_cmd.OptionRevolveAboutEdge(**data)[source][source]
Command for revolving a solid 2d about a brep edge
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'angle': <class 'kittycad.models.angle.Angle'>, 'edge_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'target': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'tolerance': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': typing.Literal['revolve_about_edge']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionRevolveAboutEdge'>, 'config': {'title': 'OptionRevolveAboutEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionRevolveAboutEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionRevolveAboutEdge'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionRevolveAboutEdge:94467866586800', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'angle': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.angle.Angle'>, 'config': {'title': 'Angle'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.angle.Angle'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.angle.Angle'>>]}, 'ref': 'kittycad.models.angle.Angle:94467862185984', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitAngle'>, 'members': [UnitAngle.DEGREES, UnitAngle.RADIANS], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_angle.UnitAngle:94467859501968', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'value': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Angle', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'edge_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'target': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'type': 'model-field'}, 'tolerance': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'revolve_about_edge', 'schema': {'expected': ['revolve_about_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionRevolveAboutEdge', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a16eb0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "target": SerField { key_py: Py( 0x00007fa14fa3c088, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "angle": SerField { key_py: Py( 0x00007fa14d9ed140, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb035e4800, ), serializer: Fields( GeneralFieldsSerializer { fields: { "unit": SerField { key_py: Py( 0x00007fa14e161380, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb03355390, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "value": SerField { key_py: Py( 0x00007fa14fa3c768, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Angle", }, ), ), required: true, }, "edge_id": SerField { key_py: Py( 0x00007fa14afd2a60, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7b1b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "revolve_about_edge", }, expected_py: None, name: "literal['revolve_about_edge']", }, ), }, ), ), required: true, }, "tolerance": SerField { key_py: Py( 0x00007fa14afd8570, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "OptionRevolveAboutEdge", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionRevolveAboutEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "angle", lookup_key: Simple { key: "angle", py_key: Py( 0x00007fa14afe7180, ), path: LookupPath( [ S( "angle", Py( 0x00007fa14afe6ca0, ), ), ], ), }, name_py: Py( 0x00007fa14d9ed140, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "unit", lookup_key: Simple { key: "unit", py_key: Py( 0x00007fa14afe7150, ), path: LookupPath( [ S( "unit", Py( 0x00007fa14afe6ee0, ), ), ], ), }, name_py: Py( 0x00007fa14e161380, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb03355390, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "degrees": 0, "radians": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b361850, ), Py( 0x00007fa14b3618b0, ), ], }, missing: None, expected_repr: "'degrees' or 'radians'", strict: false, class_repr: "UnitAngle", name: "str-enum[UnitAngle]", }, ), frozen: false, }, Field { name: "value", lookup_key: Simple { key: "value", py_key: Py( 0x00007fa14afe6f10, ), path: LookupPath( [ S( "value", Py( 0x00007fa14afe6e50, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c768, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Angle", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb035e4800, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Angle", }, ), frozen: false, }, Field { name: "edge_id", lookup_key: Simple { key: "edge_id", py_key: Py( 0x00007fa14afe6d00, ), path: LookupPath( [ S( "edge_id", Py( 0x00007fa14afe6cd0, ), ), ], ), }, name_py: Py( 0x00007fa14afd2a60, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "target", lookup_key: Simple { key: "target", py_key: Py( 0x00007fa14afe6e80, ), path: LookupPath( [ S( "target", Py( 0x00007fa14afe6df0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c088, ), validator: FunctionAfter( FunctionAfterValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), func: Py( 0x000055eb039991a0, ), config: Py( 0x00007fa14ae39900, ), name: "function-after[ModelingCmdId(), str]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "tolerance", lookup_key: Simple { key: "tolerance", py_key: Py( 0x00007fa14b100870, ), path: LookupPath( [ S( "tolerance", Py( 0x00007fa14af733b0, ), ), ], ), }, name_py: Py( 0x00007fa14afd8570, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae39900, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe6d90, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe6d60, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7b1b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "revolve_about_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7b1b0, ), ], }, expected_repr: "'revolve_about_edge'", name: "literal['revolve_about_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['revolve_about_edge']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionRevolveAboutEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a16eb0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionRevolveAboutEdge", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, angle: kittycad.models.angle.Angle, edge_id: str, target: kittycad.models.modeling_cmd_id.ModelingCmdId, tolerance: kittycad.models.length_unit.LengthUnit, type: Literal['revolve_about_edge'] = 'revolve_about_edge') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'angle': FieldInfo(annotation=Angle, required=True), 'edge_id': FieldInfo(annotation=str, required=True), 'target': FieldInfo(annotation=ModelingCmdId, required=True), 'tolerance': FieldInfo(annotation=LengthUnit, required=True), 'type': FieldInfo(annotation=Literal['revolve_about_edge'], required=False, default='revolve_about_edge')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
target:
ModelingCmdId[source]
-
tolerance:
LengthUnit[source]
- class kittycad.models.modeling_cmd.OptionSceneClearAll(**data)[source][source]
Removes all of the Objects in the scene
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['scene_clear_all']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSceneClearAll'>, 'config': {'title': 'OptionSceneClearAll'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSceneClearAll'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSceneClearAll'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSceneClearAll:94467867190112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'scene_clear_all', 'schema': {'expected': ['scene_clear_all'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSceneClearAll', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03aaa360, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7b6b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "scene_clear_all", }, expected_py: None, name: "literal['scene_clear_all']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionSceneClearAll", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSceneClearAll", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b0253b0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b025410, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7b6b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "scene_clear_all": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7b6b0, ), ], }, expected_repr: "'scene_clear_all'", name: "literal['scene_clear_all']", }, ), validate_default: false, copy_default: false, name: "default[literal['scene_clear_all']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSceneClearAll", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03aaa360, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSceneClearAll", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['scene_clear_all'] = 'scene_clear_all') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['scene_clear_all'], required=False, default='scene_clear_all')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSelectAdd(**data)[source][source]
Adds one or more entities (by UUID) to the selection.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entities': typing.List[str], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['select_add']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSelectAdd'>, 'config': {'title': 'OptionSelectAdd'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSelectAdd'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSelectAdd'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSelectAdd:94467867091728', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entities': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'select_add', 'schema': {'expected': ['select_add'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSelectAdd', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a92310, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entities": SerField { key_py: Py( 0x00007fa14d502070, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7b8b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "select_add", }, expected_py: None, name: "literal['select_add']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSelectAdd", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSelectAdd", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entities", lookup_key: Simple { key: "entities", py_key: Py( 0x00007fa14aec6f30, ), path: LookupPath( [ S( "entities", Py( 0x00007fa14aec6ef0, ), ), ], ), }, name_py: Py( 0x00007fa14d502070, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b024900, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b024930, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7b8b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select_add": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7b8b0, ), ], }, expected_repr: "'select_add'", name: "literal['select_add']", }, ), validate_default: false, copy_default: false, name: "default[literal['select_add']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSelectAdd", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a92310, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSelectAdd", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entities: List[str], type: Literal['select_add'] = 'select_add') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entities': FieldInfo(annotation=List[str], required=True), 'type': FieldInfo(annotation=Literal['select_add'], required=False, default='select_add')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSelectClear(**data)[source][source]
Clear the selection
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['select_clear']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSelectClear'>, 'config': {'title': 'OptionSelectClear'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSelectClear'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSelectClear'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSelectClear:94467868262544', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'select_clear', 'schema': {'expected': ['select_clear'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSelectClear', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03bb0090, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7b930, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "select_clear", }, expected_py: None, name: "literal['select_clear']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionSelectClear", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSelectClear", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae01920, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae01950, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7b930, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select_clear": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7b930, ), ], }, expected_repr: "'select_clear'", name: "literal['select_clear']", }, ), validate_default: false, copy_default: false, name: "default[literal['select_clear']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSelectClear", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03bb0090, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSelectClear", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['select_clear'] = 'select_clear') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['select_clear'], required=False, default='select_clear')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSelectGet(**data)[source][source]
Find all IDs of selected entities
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['select_get']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSelectGet'>, 'config': {'title': 'OptionSelectGet'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSelectGet'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSelectGet'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSelectGet:94467868271984', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'select_get', 'schema': {'expected': ['select_get'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSelectGet', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03bb2570, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7b9b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "select_get", }, expected_py: None, name: "literal['select_get']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionSelectGet", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSelectGet", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae01e00, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae01e30, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7b9b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select_get": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7b9b0, ), ], }, expected_repr: "'select_get'", name: "literal['select_get']", }, ), validate_default: false, copy_default: false, name: "default[literal['select_get']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSelectGet", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03bb2570, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSelectGet", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['select_get'] = 'select_get') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['select_get'], required=False, default='select_get')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSelectRemove(**data)[source][source]
Removes one or more entities (by UUID) from the selection.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entities': typing.List[str], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['select_remove']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSelectRemove'>, 'config': {'title': 'OptionSelectRemove'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSelectRemove'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSelectRemove'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSelectRemove:94467867181088', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entities': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'select_remove', 'schema': {'expected': ['select_remove'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSelectRemove', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03aa8020, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entities": SerField { key_py: Py( 0x00007fa14d502070, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7ba30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "select_remove", }, expected_py: None, name: "literal['select_remove']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSelectRemove", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSelectRemove", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entities", lookup_key: Simple { key: "entities", py_key: Py( 0x00007fa14aee41f0, ), path: LookupPath( [ S( "entities", Py( 0x00007fa14aee41b0, ), ), ], ), }, name_py: Py( 0x00007fa14d502070, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b024e70, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b024ea0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7ba30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select_remove": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7ba30, ), ], }, expected_repr: "'select_remove'", name: "literal['select_remove']", }, ), validate_default: false, copy_default: false, name: "default[literal['select_remove']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSelectRemove", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03aa8020, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSelectRemove", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entities: List[str], type: Literal['select_remove'] = 'select_remove') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entities': FieldInfo(annotation=List[str], required=True), 'type': FieldInfo(annotation=Literal['select_remove'], required=False, default='select_remove')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSelectReplace(**data)[source][source]
Replaces current selection with these entities (by UUID).
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entities': typing.List[str], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['select_replace']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSelectReplace'>, 'config': {'title': 'OptionSelectReplace'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSelectReplace'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSelectReplace'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSelectReplace:94467867199344', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entities': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'select_replace', 'schema': {'expected': ['select_replace'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSelectReplace', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03aac770, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7bab0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "select_replace", }, expected_py: None, name: "literal['select_replace']", }, ), }, ), ), required: true, }, "entities": SerField { key_py: Py( 0x00007fa14d502070, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSelectReplace", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSelectReplace", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entities", lookup_key: Simple { key: "entities", py_key: Py( 0x00007fa14aee63b0, ), path: LookupPath( [ S( "entities", Py( 0x00007fa14aee6370, ), ), ], ), }, name_py: Py( 0x00007fa14d502070, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b025950, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b025980, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7bab0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select_replace": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7bab0, ), ], }, expected_repr: "'select_replace'", name: "literal['select_replace']", }, ), validate_default: false, copy_default: false, name: "default[literal['select_replace']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSelectReplace", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03aac770, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSelectReplace", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entities: List[str], type: Literal['select_replace'] = 'select_replace') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entities': FieldInfo(annotation=List[str], required=True), 'type': FieldInfo(annotation=Literal['select_replace'], required=False, default='select_replace')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSelectWithPoint(**data)[source][source]
Modifies the selection by simulating a “mouse click” at the given x,y window coordinate Returns ID of whatever was selected.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'selected_at_window': <class 'kittycad.models.point2d.Point2d'>, 'selection_type': <enum 'SceneSelectionType'>, 'type': typing.Literal['select_with_point']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSelectWithPoint'>, 'config': {'title': 'OptionSelectWithPoint'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSelectWithPoint'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSelectWithPoint'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSelectWithPoint:94467867081696', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'selected_at_window': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'selection_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'SceneSelectionType'>, 'members': [SceneSelectionType.REPLACE, SceneSelectionType.ADD, SceneSelectionType.REMOVE], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.scene_selection_type.SceneSelectionType:94467866263200', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'select_with_point', 'schema': {'expected': ['select_with_point'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSelectWithPoint', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a8fbe0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "selection_type": SerField { key_py: Py( 0x00007fa14aff6e30, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb039c7ea0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "selected_at_window": SerField { key_py: Py( 0x00007fa14aff6df0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7bb70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "select_with_point", }, expected_py: None, name: "literal['select_with_point']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionSelectWithPoint", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSelectWithPoint", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "selected_at_window", lookup_key: Simple { key: "selected_at_window", py_key: Py( 0x00007fa14aeb9030, ), path: LookupPath( [ S( "selected_at_window", Py( 0x00007fa14aebac70, ), ), ], ), }, name_py: Py( 0x00007fa14aff6df0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14aeb9f40, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14aeb9f40, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), frozen: false, }, Field { name: "selection_type", lookup_key: Simple { key: "selection_type", py_key: Py( 0x00007fa14aebad70, ), path: LookupPath( [ S( "selection_type", Py( 0x00007fa14aebaeb0, ), ), ], ), }, name_py: Py( 0x00007fa14aff6e30, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb039c7ea0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "replace": 0, "add": 1, "remove": 2, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14afec7d0, ), Py( 0x00007fa14afec890, ), Py( 0x00007fa14afec8f0, ), ], }, missing: None, expected_repr: "'replace', 'add' or 'remove'", strict: false, class_repr: "SceneSelectionType", name: "str-enum[SceneSelectionType]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe57d0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe5ce0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7bb70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select_with_point": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7bb70, ), ], }, expected_repr: "'select_with_point'", name: "literal['select_with_point']", }, ), validate_default: false, copy_default: false, name: "default[literal['select_with_point']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSelectWithPoint", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a8fbe0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSelectWithPoint", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, selected_at_window: kittycad.models.point2d.Point2d, selection_type: kittycad.models.scene_selection_type.SceneSelectionType, type: Literal['select_with_point'] = 'select_with_point') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'selected_at_window': FieldInfo(annotation=Point2d, required=True), 'selection_type': FieldInfo(annotation=SceneSelectionType, required=True), 'type': FieldInfo(annotation=Literal['select_with_point'], required=False, default='select_with_point')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
selection_type:
SceneSelectionType[source]
- class kittycad.models.modeling_cmd.OptionSendObject(**data)[source][source]
Send object to front or back.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'front': <class 'bool'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['send_object']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSendObject'>, 'config': {'title': 'OptionSendObject'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSendObject'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSendObject'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSendObject:94467867558720', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'front': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'send_object', 'schema': {'expected': ['send_object'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSendObject', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b04340, ), serializer: Fields( GeneralFieldsSerializer { fields: { "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "front": SerField { key_py: Py( 0x00007fa14b0215f0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7bc70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "send_object", }, expected_py: None, name: "literal['send_object']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionSendObject", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSendObject", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "front", lookup_key: Simple { key: "front", py_key: Py( 0x00007fa14ae77360, ), path: LookupPath( [ S( "front", Py( 0x00007fa14ae77390, ), ), ], ), }, name_py: Py( 0x00007fa14b0215f0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14ad49130, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14ad490f0, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae773c0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae773f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7bc70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "send_object": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7bc70, ), ], }, expected_repr: "'send_object'", name: "literal['send_object']", }, ), validate_default: false, copy_default: false, name: "default[literal['send_object']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSendObject", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b04340, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSendObject", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, front: bool, object_id: str, type: Literal['send_object'] = 'send_object') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'front': FieldInfo(annotation=bool, required=True), 'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['send_object'], required=False, default='send_object')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSetBackgroundColor(**data)[source][source]
Set the background color of the scene.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'color': <class 'kittycad.models.color.Color'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['set_background_color']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSetBackgroundColor'>, 'config': {'title': 'OptionSetBackgroundColor'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSetBackgroundColor'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSetBackgroundColor'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSetBackgroundColor:94467867736144', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'color': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.color.Color'>, 'config': {'title': 'Color'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.color.Color'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.color.Color'>>]}, 'ref': 'kittycad.models.color.Color:94467859422064', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'a': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'b': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'g': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'r': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Color', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'set_background_color', 'schema': {'expected': ['set_background_color'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSetBackgroundColor', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b2f850, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7bfb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "set_background_color", }, expected_py: None, name: "literal['set_background_color']", }, ), }, ), ), required: true, }, "color": SerField { key_py: Py( 0x00007fa14ea1a040, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03341b70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "g": SerField { key_py: Py( 0x00007fa14fa3de10, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "a": SerField { key_py: Py( 0x00007fa14fa3dcf0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "b": SerField { key_py: Py( 0x00007fa14fa3dd20, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "r": SerField { key_py: Py( 0x00007fa14fa3e020, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Color", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSetBackgroundColor", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSetBackgroundColor", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "color", lookup_key: Simple { key: "color", py_key: Py( 0x00007fa14ad55e90, ), path: LookupPath( [ S( "color", Py( 0x00007fa14ad55ec0, ), ), ], ), }, name_py: Py( 0x00007fa14ea1a040, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "a", lookup_key: Simple { key: "a", py_key: Py( 0x00007fa14fa3dcf0, ), path: LookupPath( [ S( "a", Py( 0x00007fa14fa3dcf0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3dcf0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "b", lookup_key: Simple { key: "b", py_key: Py( 0x00007fa14fa3dd20, ), path: LookupPath( [ S( "b", Py( 0x00007fa14fa3dd20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3dd20, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "g", lookup_key: Simple { key: "g", py_key: Py( 0x00007fa14fa3de10, ), path: LookupPath( [ S( "g", Py( 0x00007fa14fa3de10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3de10, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "r", lookup_key: Simple { key: "r", py_key: Py( 0x00007fa14fa3e020, ), path: LookupPath( [ S( "r", Py( 0x00007fa14fa3e020, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e020, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Color", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03341b70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Color", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad55ef0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad55f20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7bfb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "set_background_color": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7bfb0, ), ], }, expected_repr: "'set_background_color'", name: "literal['set_background_color']", }, ), validate_default: false, copy_default: false, name: "default[literal['set_background_color']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSetBackgroundColor", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b2f850, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSetBackgroundColor", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, color: kittycad.models.color.Color, type: Literal['set_background_color'] = 'set_background_color') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'color': FieldInfo(annotation=Color, required=True), 'type': FieldInfo(annotation=Literal['set_background_color'], required=False, default='set_background_color')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSetCurrentToolProperties(**data)[source][source]
Set the properties of the tool lines for the scene.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'color': typing.Optional[kittycad.models.color.Color], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['set_current_tool_properties']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSetCurrentToolProperties'>, 'config': {'title': 'OptionSetCurrentToolProperties'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSetCurrentToolProperties'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSetCurrentToolProperties'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSetCurrentToolProperties:94467867744160', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'color': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.color.Color'>, 'config': {'title': 'Color'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.color.Color'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.color.Color'>>]}, 'ref': 'kittycad.models.color.Color:94467859422064', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'a': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'b': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'g': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'r': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Color', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'set_current_tool_properties', 'schema': {'expected': ['set_current_tool_properties'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSetCurrentToolProperties', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b317a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b0f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "set_current_tool_properties", }, expected_py: None, name: "literal['set_current_tool_properties']", }, ), }, ), ), required: true, }, "color": SerField { key_py: Py( 0x00007fa14ea1a040, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055eb03341b70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "g": SerField { key_py: Py( 0x00007fa14fa3de10, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "b": SerField { key_py: Py( 0x00007fa14fa3dd20, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "r": SerField { key_py: Py( 0x00007fa14fa3e020, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "a": SerField { key_py: Py( 0x00007fa14fa3dcf0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Color", }, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSetCurrentToolProperties", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSetCurrentToolProperties", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "color", lookup_key: Simple { key: "color", py_key: Py( 0x00007fa14ad55b00, ), path: LookupPath( [ S( "color", Py( 0x00007fa14ad55ad0, ), ), ], ), }, name_py: Py( 0x00007fa14ea1a040, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "a", lookup_key: Simple { key: "a", py_key: Py( 0x00007fa14fa3dcf0, ), path: LookupPath( [ S( "a", Py( 0x00007fa14fa3dcf0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3dcf0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "b", lookup_key: Simple { key: "b", py_key: Py( 0x00007fa14fa3dd20, ), path: LookupPath( [ S( "b", Py( 0x00007fa14fa3dd20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3dd20, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "g", lookup_key: Simple { key: "g", py_key: Py( 0x00007fa14fa3de10, ), path: LookupPath( [ S( "g", Py( 0x00007fa14fa3de10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3de10, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "r", lookup_key: Simple { key: "r", py_key: Py( 0x00007fa14fa3e020, ), path: LookupPath( [ S( "r", Py( 0x00007fa14fa3e020, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e020, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Color", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03341b70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Color", }, ), name: "nullable[Color]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Color]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad55c20, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad55bf0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b0f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "set_current_tool_properties": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b0f0, ), ], }, expected_repr: "'set_current_tool_properties'", name: "literal['set_current_tool_properties']", }, ), validate_default: false, copy_default: false, name: "default[literal['set_current_tool_properties']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSetCurrentToolProperties", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b317a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSetCurrentToolProperties", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, color: Optional[kittycad.models.color.Color] = None, type: Literal['set_current_tool_properties'] = 'set_current_tool_properties') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'color': FieldInfo(annotation=Union[Color, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['set_current_tool_properties'], required=False, default='set_current_tool_properties')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSetDefaultSystemProperties(**data)[source][source]
Set the default system properties used when a specific property isn’t set.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'color': typing.Optional[kittycad.models.color.Color], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['set_default_system_properties']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSetDefaultSystemProperties'>, 'config': {'title': 'OptionSetDefaultSystemProperties'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSetDefaultSystemProperties'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSetDefaultSystemProperties'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSetDefaultSystemProperties:94467867759344', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'color': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.color.Color'>, 'config': {'title': 'Color'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.color.Color'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.color.Color'>>]}, 'ref': 'kittycad.models.color.Color:94467859422064', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'a': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'b': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'g': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'r': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Color', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'set_default_system_properties', 'schema': {'expected': ['set_default_system_properties'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSetDefaultSystemProperties', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b352f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "color": SerField { key_py: Py( 0x00007fa14ea1a040, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055eb03341b70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "g": SerField { key_py: Py( 0x00007fa14fa3de10, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "r": SerField { key_py: Py( 0x00007fa14fa3e020, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "b": SerField { key_py: Py( 0x00007fa14fa3dd20, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "a": SerField { key_py: Py( 0x00007fa14fa3dcf0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Color", }, ), }, ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b190, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "set_default_system_properties", }, expected_py: None, name: "literal['set_default_system_properties']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSetDefaultSystemProperties", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSetDefaultSystemProperties", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "color", lookup_key: Simple { key: "color", py_key: Py( 0x00007fa14ad55260, ), path: LookupPath( [ S( "color", Py( 0x00007fa14ad55200, ), ), ], ), }, name_py: Py( 0x00007fa14ea1a040, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "a", lookup_key: Simple { key: "a", py_key: Py( 0x00007fa14fa3dcf0, ), path: LookupPath( [ S( "a", Py( 0x00007fa14fa3dcf0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3dcf0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "b", lookup_key: Simple { key: "b", py_key: Py( 0x00007fa14fa3dd20, ), path: LookupPath( [ S( "b", Py( 0x00007fa14fa3dd20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3dd20, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "g", lookup_key: Simple { key: "g", py_key: Py( 0x00007fa14fa3de10, ), path: LookupPath( [ S( "g", Py( 0x00007fa14fa3de10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3de10, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "r", lookup_key: Simple { key: "r", py_key: Py( 0x00007fa14fa3e020, ), path: LookupPath( [ S( "r", Py( 0x00007fa14fa3e020, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e020, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Color", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03341b70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Color", }, ), name: "nullable[Color]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Color]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad551a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad55170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b190, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "set_default_system_properties": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b190, ), ], }, expected_repr: "'set_default_system_properties'", name: "literal['set_default_system_properties']", }, ), validate_default: false, copy_default: false, name: "default[literal['set_default_system_properties']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSetDefaultSystemProperties", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b352f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSetDefaultSystemProperties", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, color: Optional[kittycad.models.color.Color] = None, type: Literal['set_default_system_properties'] = 'set_default_system_properties') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'color': FieldInfo(annotation=Union[Color, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['set_default_system_properties'], required=False, default='set_default_system_properties')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSetSceneUnits(**data)[source][source]
Set the units of the scene. For all following commands, the units will be interpreted as the given units.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['set_scene_units'], 'unit': <enum 'UnitLength'>}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSetSceneUnits'>, 'config': {'title': 'OptionSetSceneUnits'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSetSceneUnits'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSetSceneUnits'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSetSceneUnits:94467867959648', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'set_scene_units', 'schema': {'expected': ['set_scene_units'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitLength'>, 'members': [UnitLength.CM, UnitLength.FT, UnitLength.IN, UnitLength.M, UnitLength.MM, UnitLength.YD], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_length.UnitLength:94467861018320', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'OptionSetSceneUnits', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b66160, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7c1b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "set_scene_units", }, expected_py: None, name: "literal['set_scene_units']", }, ), }, ), ), required: true, }, "unit": SerField { key_py: Py( 0x00007fa14e161380, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb034c76d0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSetSceneUnits", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSetSceneUnits", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc5e60, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc5f80, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7c1b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "set_scene_units": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7c1b0, ), ], }, expected_repr: "'set_scene_units'", name: "literal['set_scene_units']", }, ), validate_default: false, copy_default: false, name: "default[literal['set_scene_units']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "unit", lookup_key: Simple { key: "unit", py_key: Py( 0x00007fa14adc5fb0, ), path: LookupPath( [ S( "unit", Py( 0x00007fa14adc5fe0, ), ), ], ), }, name_py: Py( 0x00007fa14e161380, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb034c76d0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "cm": 0, "ft": 1, "in": 2, "m": 3, "mm": 4, "yd": 5, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c4c50, ), Py( 0x00007fa14b3c51f0, ), Py( 0x00007fa14b3c52b0, ), Py( 0x00007fa14b3c5310, ), Py( 0x00007fa14b3c5370, ), Py( 0x00007fa14b3c53d0, ), ], }, missing: None, expected_repr: "'cm', 'ft', 'in', 'm', 'mm' or 'yd'", strict: false, class_repr: "UnitLength", name: "str-enum[UnitLength]", }, ), frozen: false, }, ], model_name: "OptionSetSceneUnits", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b66160, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSetSceneUnits", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['set_scene_units'] = 'set_scene_units', unit: kittycad.models.unit_length.UnitLength) -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['set_scene_units'], required=False, default='set_scene_units'), 'unit': FieldInfo(annotation=UnitLength, required=True)}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
unit:
UnitLength[source]
- class kittycad.models.modeling_cmd.OptionSetSelectionFilter(**data)[source][source]
What kind of entities can be selected?
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'filter': typing.List[kittycad.models.entity_type.EntityType], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['set_selection_filter']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSetSelectionFilter'>, 'config': {'title': 'OptionSetSelectionFilter'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSetSelectionFilter'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSetSelectionFilter'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSetSelectionFilter:94467849608080', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'filter': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'cls': <enum 'EntityType'>, 'members': [EntityType.ENTITY, EntityType.OBJECT, EntityType.PATH, EntityType.CURVE, EntityType.SOLID2D, EntityType.SOLID3D, EntityType.EDGE, EntityType.FACE, EntityType.PLANE, EntityType.VERTEX], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.entity_type.EntityType:94467864596176', 'sub_type': 'str', 'type': 'enum'}, 'type': 'list'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'set_selection_filter', 'schema': {'expected': ['set_selection_filter'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSetSelectionFilter', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb029e5b90, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7c270, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "set_selection_filter", }, expected_py: None, name: "literal['set_selection_filter']", }, ), }, ), ), required: true, }, "filter": SerField { key_py: Py( 0x00007fa14ef96430, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Enum( EnumSerializer { class: Py( 0x000055eb03830ed0, ), serializer: Some( Str( StrSerializer, ), ), }, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[enum]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSetSelectionFilter", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSetSelectionFilter", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "filter", lookup_key: Simple { key: "filter", py_key: Py( 0x00007fa14adc6d90, ), path: LookupPath( [ S( "filter", Py( 0x00007fa14adc7120, ), ), ], ), }, name_py: Py( 0x00007fa14ef96430, ), validator: List( ListValidator { strict: false, item_validator: Some( StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb03830ed0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "curve": 3, "entity": 0, "solid3d": 5, "path": 2, "plane": 8, "vertex": 9, "face": 7, "solid2d": 4, "edge": 6, "object": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b0411f0, ), Py( 0x00007fa14b041250, ), Py( 0x00007fa14b0412b0, ), Py( 0x00007fa14b041310, ), Py( 0x00007fa14b041370, ), Py( 0x00007fa14b0413d0, ), Py( 0x00007fa14b041430, ), Py( 0x00007fa14b041490, ), Py( 0x00007fa14b0414f0, ), Py( 0x00007fa14b041550, ), ], }, missing: None, expected_repr: "'entity', 'object', 'path', 'curve', 'solid2d', 'solid3d', 'edge', 'face', 'plane' or 'vertex'", strict: false, class_repr: "EntityType", name: "str-enum[EntityType]", }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc5410, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc52c0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7c270, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "set_selection_filter": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7c270, ), ], }, expected_repr: "'set_selection_filter'", name: "literal['set_selection_filter']", }, ), validate_default: false, copy_default: false, name: "default[literal['set_selection_filter']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSetSelectionFilter", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb029e5b90, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSetSelectionFilter", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, filter: List[kittycad.models.entity_type.EntityType], type: Literal['set_selection_filter'] = 'set_selection_filter') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
-
filter:
List[EntityType][source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'filter': FieldInfo(annotation=List[EntityType], required=True), 'type': FieldInfo(annotation=Literal['set_selection_filter'], required=False, default='set_selection_filter')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSetSelectionType(**data)[source][source]
When you select some entity with the current tool, what should happen to the entity?
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'selection_type': <enum 'SceneSelectionType'>, 'type': typing.Literal['set_selection_type']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSetSelectionType'>, 'config': {'title': 'OptionSetSelectionType'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSetSelectionType'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSetSelectionType'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSetSelectionType:94467868107792', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'selection_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'SceneSelectionType'>, 'members': [SceneSelectionType.REPLACE, SceneSelectionType.ADD, SceneSelectionType.REMOVE], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.scene_selection_type.SceneSelectionType:94467866263200', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'set_selection_type', 'schema': {'expected': ['set_selection_type'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSetSelectionType', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b8a410, ), serializer: Fields( GeneralFieldsSerializer { fields: { "selection_type": SerField { key_py: Py( 0x00007fa14aff6e30, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb039c7ea0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7c370, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "set_selection_type", }, expected_py: None, name: "literal['set_selection_type']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSetSelectionType", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSetSelectionType", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "selection_type", lookup_key: Simple { key: "selection_type", py_key: Py( 0x00007fa14ada95b0, ), path: LookupPath( [ S( "selection_type", Py( 0x00007fa14affcbf0, ), ), ], ), }, name_py: Py( 0x00007fa14aff6e30, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb039c7ea0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "replace": 0, "remove": 2, "add": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14afec7d0, ), Py( 0x00007fa14afec890, ), Py( 0x00007fa14afec8f0, ), ], }, missing: None, expected_repr: "'replace', 'add' or 'remove'", strict: false, class_repr: "SceneSelectionType", name: "str-enum[SceneSelectionType]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc7960, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc7930, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7c370, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "set_selection_type": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7c370, ), ], }, expected_repr: "'set_selection_type'", name: "literal['set_selection_type']", }, ), validate_default: false, copy_default: false, name: "default[literal['set_selection_type']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSetSelectionType", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b8a410, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSetSelectionType", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, selection_type: kittycad.models.scene_selection_type.SceneSelectionType, type: Literal['set_selection_type'] = 'set_selection_type') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'selection_type': FieldInfo(annotation=SceneSelectionType, required=True), 'type': FieldInfo(annotation=Literal['set_selection_type'], required=False, default='set_selection_type')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
selection_type:
SceneSelectionType[source]
- class kittycad.models.modeling_cmd.OptionSetTool(**data)[source][source]
Set the current tool.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'tool': <enum 'SceneToolType'>, 'type': typing.Literal['set_tool']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSetTool'>, 'config': {'title': 'OptionSetTool'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSetTool'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSetTool'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSetTool:94467867626416', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'tool': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'SceneToolType'>, 'members': [SceneToolType.CAMERA_REVOLVE, SceneToolType.SELECT, SceneToolType.MOVE, SceneToolType.SKETCH_LINE, SceneToolType.SKETCH_TANGENTIAL_ARC, SceneToolType.SKETCH_CURVE, SceneToolType.SKETCH_CURVE_MOD], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.scene_tool_type.SceneToolType:94467866267360', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'set_tool', 'schema': {'expected': ['set_tool'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSetTool', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b14bb0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "tool": SerField { key_py: Py( 0x00007fa14b022340, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb039c8ee0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7c430, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "set_tool", }, expected_py: None, name: "literal['set_tool']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSetTool", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSetTool", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "tool", lookup_key: Simple { key: "tool", py_key: Py( 0x00007fa14ae77570, ), path: LookupPath( [ S( "tool", Py( 0x00007fa14ae76eb0, ), ), ], ), }, name_py: Py( 0x00007fa14b022340, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb039c8ee0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select": 1, "sketch_line": 3, "camera_revolve": 0, "sketch_tangential_arc": 4, "move": 2, "sketch_curve": 5, "sketch_curve_mod": 6, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14afecad0, ), Py( 0x00007fa14afeca10, ), Py( 0x00007fa14afecb30, ), Py( 0x00007fa14afecb90, ), Py( 0x00007fa14afecbf0, ), Py( 0x00007fa14afecc50, ), Py( 0x00007fa14afeccb0, ), ], }, missing: None, expected_repr: "'camera_revolve', 'select', 'move', 'sketch_line', 'sketch_tangential_arc', 'sketch_curve' or 'sketch_curve_mod'", strict: false, class_repr: "SceneToolType", name: "str-enum[SceneToolType]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae767c0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae775a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7c430, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "set_tool": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7c430, ), ], }, expected_repr: "'set_tool'", name: "literal['set_tool']", }, ), validate_default: false, copy_default: false, name: "default[literal['set_tool']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSetTool", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b14bb0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSetTool", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, tool: kittycad.models.scene_tool_type.SceneToolType, type: Literal['set_tool'] = 'set_tool') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'tool': FieldInfo(annotation=SceneToolType, required=True), 'type': FieldInfo(annotation=Literal['set_tool'], required=False, default='set_tool')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
tool:
SceneToolType[source]
- class kittycad.models.modeling_cmd.OptionSketchModeDisable(**data)[source][source]
Disable sketch mode. If you are sketching on a face, be sure to not disable sketch mode until you have extruded. Otherwise, your object will not be fused with the face.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['sketch_mode_disable']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSketchModeDisable'>, 'config': {'title': 'OptionSketchModeDisable'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSketchModeDisable'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSketchModeDisable'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSketchModeDisable:94467867667600', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'sketch_mode_disable', 'schema': {'expected': ['sketch_mode_disable'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSketchModeDisable', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b1ec90, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7c630, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "sketch_mode_disable", }, expected_py: None, name: "literal['sketch_mode_disable']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionSketchModeDisable", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSketchModeDisable", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad54810, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad54840, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7c630, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "sketch_mode_disable": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7c630, ), ], }, expected_repr: "'sketch_mode_disable'", name: "literal['sketch_mode_disable']", }, ), validate_default: false, copy_default: false, name: "default[literal['sketch_mode_disable']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSketchModeDisable", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b1ec90, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSketchModeDisable", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['sketch_mode_disable'] = 'sketch_mode_disable') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['sketch_mode_disable'], required=False, default='sketch_mode_disable')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSolid2DAddHole(**data)[source][source]
Add a hole to a Solid2d object before extruding it.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'hole_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['solid2d_add_hole']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSolid2DAddHole'>, 'config': {'title': 'OptionSolid2DAddHole'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSolid2DAddHole'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSolid2DAddHole'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSolid2DAddHole:94467867410640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'hole_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid2d_add_hole', 'schema': {'expected': ['solid2d_add_hole'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid2DAddHole', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ae00d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7c730, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid2d_add_hole", }, expected_py: None, name: "literal['solid2d_add_hole']", }, ), }, ), ), required: true, }, "hole_id": SerField { key_py: Py( 0x00007fa14afe7cc0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionSolid2DAddHole", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid2DAddHole", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "hole_id", lookup_key: Simple { key: "hole_id", py_key: Py( 0x00007fa14ae74c90, ), path: LookupPath( [ S( "hole_id", Py( 0x00007fa14ae74cf0, ), ), ], ), }, name_py: Py( 0x00007fa14afe7cc0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14af12df0, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14af12db0, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae74d50, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae74db0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7c730, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid2d_add_hole": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7c730, ), ], }, expected_repr: "'solid2d_add_hole'", name: "literal['solid2d_add_hole']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid2d_add_hole']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid2DAddHole", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ae00d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid2DAddHole", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, hole_id: str, object_id: str, type: Literal['solid2d_add_hole'] = 'solid2d_add_hole') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'hole_id': FieldInfo(annotation=str, required=True), 'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['solid2d_add_hole'], required=False, default='solid2d_add_hole')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSolid3DFilletEdge(**data)[source][source]
Fillets the given edge with the specified radius.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'cut_type': <enum 'CutType'>, 'edge_id': <class 'str'>, 'face_id': typing.Optional[str], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'radius': <class 'kittycad.models.length_unit.LengthUnit'>, 'tolerance': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': typing.Literal['solid3d_fillet_edge']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSolid3DFilletEdge'>, 'config': {'title': 'OptionSolid3DFilletEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSolid3DFilletEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSolid3DFilletEdge'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSolid3DFilletEdge:94467867481856', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'cut_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'fillet', 'schema': {'cls': <enum 'CutType'>, 'members': [CutType.FILLET, CutType.CHAMFER], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.cut_type.CutType:94467864271312', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'edge_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'face_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'radius': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'tolerance': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_fillet_edge', 'schema': {'expected': ['solid3d_fillet_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DFilletEdge', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03af1700, ), serializer: Fields( GeneralFieldsSerializer { fields: { "face_id": SerField { key_py: Py( 0x00007fa14b32c8a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "cut_type": SerField { key_py: Py( 0x00007fa14beb7170, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14b188c60, ), ), serializer: Enum( EnumSerializer { class: Py( 0x000055eb037e19d0, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "radius": SerField { key_py: Py( 0x00007fa14b020cf0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "tolerance": SerField { key_py: Py( 0x00007fa14afd8570, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7c7f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_fillet_edge", }, expected_py: None, name: "literal['solid3d_fillet_edge']", }, ), }, ), ), required: true, }, "edge_id": SerField { key_py: Py( 0x00007fa14afd2a60, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "OptionSolid3DFilletEdge", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DFilletEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "cut_type", lookup_key: Simple { key: "cut_type", py_key: Py( 0x00007fa14af386f0, ), path: LookupPath( [ S( "cut_type", Py( 0x00007fa14ad2f7f0, ), ), ], ), }, name_py: Py( 0x00007fa14beb7170, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14b188c60, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb037e19d0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "fillet": 0, "chamfer": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b1a1550, ), Py( 0x00007fa14b1a15b0, ), ], }, missing: None, expected_repr: "'fillet' or 'chamfer'", strict: false, class_repr: "CutType", name: "str-enum[CutType]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[CutType]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "edge_id", lookup_key: Simple { key: "edge_id", py_key: Py( 0x00007fa14b025590, ), path: LookupPath( [ S( "edge_id", Py( 0x00007fa14b027180, ), ), ], ), }, name_py: Py( 0x00007fa14afd2a60, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "face_id", lookup_key: Simple { key: "face_id", py_key: Py( 0x00007fa14b024750, ), path: LookupPath( [ S( "face_id", Py( 0x00007fa14b025b60, ), ), ], ), }, name_py: Py( 0x00007fa14b32c8a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14ad2f870, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14ad2f830, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "radius", lookup_key: Simple { key: "radius", py_key: Py( 0x00007fa14b025b90, ), path: LookupPath( [ S( "radius", Py( 0x00007fa14b024540, ), ), ], ), }, name_py: Py( 0x00007fa14b020cf0, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ad2f780, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "tolerance", lookup_key: Simple { key: "tolerance", py_key: Py( 0x00007fa14ad2f8b0, ), path: LookupPath( [ S( "tolerance", Py( 0x00007fa14ad2f8f0, ), ), ], ), }, name_py: Py( 0x00007fa14afd8570, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ad2f780, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b026ac0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b0245d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7c7f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_fillet_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7c7f0, ), ], }, expected_repr: "'solid3d_fillet_edge'", name: "literal['solid3d_fillet_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_fillet_edge']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DFilletEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03af1700, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DFilletEdge", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, cut_type: kittycad.models.cut_type.CutType = 'fillet', edge_id: str, face_id: Optional[str] = None, object_id: str, radius: kittycad.models.length_unit.LengthUnit, tolerance: kittycad.models.length_unit.LengthUnit, type: Literal['solid3d_fillet_edge'] = 'solid3d_fillet_edge') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'cut_type': FieldInfo(annotation=CutType, required=False, default='fillet'), 'edge_id': FieldInfo(annotation=str, required=True), 'face_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'object_id': FieldInfo(annotation=str, required=True), 'radius': FieldInfo(annotation=LengthUnit, required=True), 'tolerance': FieldInfo(annotation=LengthUnit, required=True), 'type': FieldInfo(annotation=Literal['solid3d_fillet_edge'], required=False, default='solid3d_fillet_edge')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
-
radius:
LengthUnit[source]
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
tolerance:
LengthUnit[source]
- class kittycad.models.modeling_cmd.OptionSolid3DGetAllEdgeFaces(**data)[source][source]
Gets all faces which use the given edge.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'edge_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['solid3d_get_all_edge_faces']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSolid3DGetAllEdgeFaces'>, 'config': {'title': 'OptionSolid3DGetAllEdgeFaces'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSolid3DGetAllEdgeFaces'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSolid3DGetAllEdgeFaces'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSolid3DGetAllEdgeFaces:94467867402032', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edge_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_all_edge_faces', 'schema': {'expected': ['solid3d_get_all_edge_faces'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetAllEdgeFaces', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03addf30, ), serializer: Fields( GeneralFieldsSerializer { fields: { "edge_id": SerField { key_py: Py( 0x00007fa14afd2a60, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b230, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_all_edge_faces", }, expected_py: None, name: "literal['solid3d_get_all_edge_faces']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetAllEdgeFaces", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetAllEdgeFaces", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edge_id", lookup_key: Simple { key: "edge_id", py_key: Py( 0x00007fa14b027d20, ), path: LookupPath( [ S( "edge_id", Py( 0x00007fa14b027d50, ), ), ], ), }, name_py: Py( 0x00007fa14afd2a60, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14af11930, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14af118f0, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b027d80, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b027db0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b230, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_all_edge_faces": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b230, ), ], }, expected_repr: "'solid3d_get_all_edge_faces'", name: "literal['solid3d_get_all_edge_faces']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_all_edge_faces']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetAllEdgeFaces", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03addf30, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DGetAllEdgeFaces", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, edge_id: str, object_id: str, type: Literal['solid3d_get_all_edge_faces'] = 'solid3d_get_all_edge_faces') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'edge_id': FieldInfo(annotation=str, required=True), 'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_all_edge_faces'], required=False, default='solid3d_get_all_edge_faces')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSolid3DGetAllOppositeEdges(**data)[source][source]
Gets all edges which are opposite the given edge, across all possible faces.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'along_vector': typing.Optional[kittycad.models.point3d.Point3d], 'edge_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['solid3d_get_all_opposite_edges']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSolid3DGetAllOppositeEdges'>, 'config': {'title': 'OptionSolid3DGetAllOppositeEdges'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSolid3DGetAllOppositeEdges'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSolid3DGetAllOppositeEdges'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSolid3DGetAllOppositeEdges:94467867420064', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'along_vector': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'edge_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_all_opposite_edges', 'schema': {'expected': ['solid3d_get_all_opposite_edges'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetAllOppositeEdges', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ae25a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "edge_id": SerField { key_py: Py( 0x00007fa14afd2a60, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b280, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_all_opposite_edges", }, expected_py: None, name: "literal['solid3d_get_all_opposite_edges']", }, ), }, ), ), required: true, }, "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "along_vector": SerField { key_py: Py( 0x00007fa14b01cbb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055eb0330e750, ), serializer: Fields( GeneralFieldsSerializer { fields: { "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetAllOppositeEdges", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetAllOppositeEdges", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "along_vector", lookup_key: Simple { key: "along_vector", py_key: Py( 0x00007fa14af111f0, ), path: LookupPath( [ S( "along_vector", Py( 0x00007fa14af116b0, ), ), ], ), }, name_py: Py( 0x00007fa14b01cbb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0330e750, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point3d", }, ), name: "nullable[Point3d]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Point3d]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "edge_id", lookup_key: Simple { key: "edge_id", py_key: Py( 0x00007fa14b0274e0, ), path: LookupPath( [ S( "edge_id", Py( 0x00007fa14b0274b0, ), ), ], ), }, name_py: Py( 0x00007fa14afd2a60, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14af11570, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14af114b0, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b027450, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b027420, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b280, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_all_opposite_edges": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b280, ), ], }, expected_repr: "'solid3d_get_all_opposite_edges'", name: "literal['solid3d_get_all_opposite_edges']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_all_opposite_edges']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetAllOppositeEdges", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ae25a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DGetAllOppositeEdges", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, along_vector: Optional[kittycad.models.point3d.Point3d] = None, edge_id: str, object_id: str, type: Literal['solid3d_get_all_opposite_edges'] = 'solid3d_get_all_opposite_edges') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'along_vector': FieldInfo(annotation=Union[Point3d, NoneType], required=False, default=None), 'edge_id': FieldInfo(annotation=str, required=True), 'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_all_opposite_edges'], required=False, default='solid3d_get_all_opposite_edges')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSolid3DGetCommonEdge(**data)[source][source]
Gets the shared edge between these two faces if it exists
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'face_ids': typing.List[str], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['solid3d_get_common_edge']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSolid3DGetCommonEdge'>, 'config': {'title': 'OptionSolid3DGetCommonEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSolid3DGetCommonEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSolid3DGetCommonEdge'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSolid3DGetCommonEdge:94467867470352', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'face_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_common_edge', 'schema': {'expected': ['solid3d_get_common_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetCommonEdge', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03aeea10, ), serializer: Fields( GeneralFieldsSerializer { fields: { "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7ca30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_common_edge", }, expected_py: None, name: "literal['solid3d_get_common_edge']", }, ), }, ), ), required: true, }, "face_ids": SerField { key_py: Py( 0x00007fa14afe06b0, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetCommonEdge", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetCommonEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "face_ids", lookup_key: Simple { key: "face_ids", py_key: Py( 0x00007fa14ad2cdb0, ), path: LookupPath( [ S( "face_ids", Py( 0x00007fa14ad2cd70, ), ), ], ), }, name_py: Py( 0x00007fa14afe06b0, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14ad2ce30, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14ad2cdf0, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae76130, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae76160, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7ca30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_common_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7ca30, ), ], }, expected_repr: "'solid3d_get_common_edge'", name: "literal['solid3d_get_common_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_common_edge']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetCommonEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03aeea10, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DGetCommonEdge", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, face_ids: List[str], object_id: str, type: Literal['solid3d_get_common_edge'] = 'solid3d_get_common_edge') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'face_ids': FieldInfo(annotation=List[str], required=True), 'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_common_edge'], required=False, default='solid3d_get_common_edge')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSolid3DGetExtrusionFaceInfo(**data)[source][source]
Get a concise description of all of an extrusion’s faces.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'edge_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['solid3d_get_extrusion_face_info']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSolid3DGetExtrusionFaceInfo'>, 'config': {'title': 'OptionSolid3DGetExtrusionFaceInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSolid3DGetExtrusionFaceInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSolid3DGetExtrusionFaceInfo'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSolid3DGetExtrusionFaceInfo:94467868253488', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edge_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_extrusion_face_info', 'schema': {'expected': ['solid3d_get_extrusion_face_info'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetExtrusionFaceInfo', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03badd30, ), serializer: Fields( GeneralFieldsSerializer { fields: { "edge_id": SerField { key_py: Py( 0x00007fa14afd2a60, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b320, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_extrusion_face_info", }, expected_py: None, name: "literal['solid3d_get_extrusion_face_info']", }, ), }, ), ), required: true, }, "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetExtrusionFaceInfo", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetExtrusionFaceInfo", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edge_id", lookup_key: Simple { key: "edge_id", py_key: Py( 0x00007fa14ae013e0, ), path: LookupPath( [ S( "edge_id", Py( 0x00007fa14ae01410, ), ), ], ), }, name_py: Py( 0x00007fa14afd2a60, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14ae34af0, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14ae197f0, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae01440, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae01470, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b320, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_extrusion_face_info": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b320, ), ], }, expected_repr: "'solid3d_get_extrusion_face_info'", name: "literal['solid3d_get_extrusion_face_info']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_extrusion_face_info']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetExtrusionFaceInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03badd30, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DGetExtrusionFaceInfo", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, edge_id: str, object_id: str, type: Literal['solid3d_get_extrusion_face_info'] = 'solid3d_get_extrusion_face_info') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'edge_id': FieldInfo(annotation=str, required=True), 'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_extrusion_face_info'], required=False, default='solid3d_get_extrusion_face_info')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSolid3DGetNextAdjacentEdge(**data)[source][source]
Gets the next adjacent edge for the given edge, along the given face.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'edge_id': <class 'str'>, 'face_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['solid3d_get_next_adjacent_edge']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSolid3DGetNextAdjacentEdge'>, 'config': {'title': 'OptionSolid3DGetNextAdjacentEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSolid3DGetNextAdjacentEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSolid3DGetNextAdjacentEdge'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSolid3DGetNextAdjacentEdge:94467867447408', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edge_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'face_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_next_adjacent_edge', 'schema': {'expected': ['solid3d_get_next_adjacent_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetNextAdjacentEdge', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ae9070, ), serializer: Fields( GeneralFieldsSerializer { fields: { "edge_id": SerField { key_py: Py( 0x00007fa14afd2a60, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "face_id": SerField { key_py: Py( 0x00007fa14b32c8a0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b3c0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_next_adjacent_edge", }, expected_py: None, name: "literal['solid3d_get_next_adjacent_edge']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetNextAdjacentEdge", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetNextAdjacentEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edge_id", lookup_key: Simple { key: "edge_id", py_key: Py( 0x00007fa14ae75470, ), path: LookupPath( [ S( "edge_id", Py( 0x00007fa14ae754a0, ), ), ], ), }, name_py: Py( 0x00007fa14afd2a60, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "face_id", lookup_key: Simple { key: "face_id", py_key: Py( 0x00007fa14ae754d0, ), path: LookupPath( [ S( "face_id", Py( 0x00007fa14ae75500, ), ), ], ), }, name_py: Py( 0x00007fa14b32c8a0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14af1e030, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14af1dff0, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae75530, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae75560, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b3c0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_next_adjacent_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b3c0, ), ], }, expected_repr: "'solid3d_get_next_adjacent_edge'", name: "literal['solid3d_get_next_adjacent_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_next_adjacent_edge']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetNextAdjacentEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ae9070, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DGetNextAdjacentEdge", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, edge_id: str, face_id: str, object_id: str, type: Literal['solid3d_get_next_adjacent_edge'] = 'solid3d_get_next_adjacent_edge') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'edge_id': FieldInfo(annotation=str, required=True), 'face_id': FieldInfo(annotation=str, required=True), 'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_next_adjacent_edge'], required=False, default='solid3d_get_next_adjacent_edge')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSolid3DGetOppositeEdge(**data)[source][source]
Gets the edge opposite the given edge, along the given face.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'edge_id': <class 'str'>, 'face_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['solid3d_get_opposite_edge']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSolid3DGetOppositeEdge'>, 'config': {'title': 'OptionSolid3DGetOppositeEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSolid3DGetOppositeEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSolid3DGetOppositeEdge'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSolid3DGetOppositeEdge:94467867429456', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edge_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'face_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_opposite_edge', 'schema': {'expected': ['solid3d_get_opposite_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetOppositeEdge', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ae4a50, ), serializer: Fields( GeneralFieldsSerializer { fields: { "face_id": SerField { key_py: Py( 0x00007fa14b32c8a0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "edge_id": SerField { key_py: Py( 0x00007fa14afd2a60, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b460, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_opposite_edge", }, expected_py: None, name: "literal['solid3d_get_opposite_edge']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetOppositeEdge", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetOppositeEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edge_id", lookup_key: Simple { key: "edge_id", py_key: Py( 0x00007fa14ae74ed0, ), path: LookupPath( [ S( "edge_id", Py( 0x00007fa14ae74fc0, ), ), ], ), }, name_py: Py( 0x00007fa14afd2a60, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "face_id", lookup_key: Simple { key: "face_id", py_key: Py( 0x00007fa14ae75050, ), path: LookupPath( [ S( "face_id", Py( 0x00007fa14ae75020, ), ), ], ), }, name_py: Py( 0x00007fa14b32c8a0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14af1c8f0, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14af1c8b0, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae74ff0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae74f90, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b460, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_opposite_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b460, ), ], }, expected_repr: "'solid3d_get_opposite_edge'", name: "literal['solid3d_get_opposite_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_opposite_edge']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetOppositeEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ae4a50, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DGetOppositeEdge", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, edge_id: str, face_id: str, object_id: str, type: Literal['solid3d_get_opposite_edge'] = 'solid3d_get_opposite_edge') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'edge_id': FieldInfo(annotation=str, required=True), 'face_id': FieldInfo(annotation=str, required=True), 'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_opposite_edge'], required=False, default='solid3d_get_opposite_edge')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSolid3DGetPrevAdjacentEdge(**data)[source][source]
Gets the previous adjacent edge for the given edge, along the given face.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'edge_id': <class 'str'>, 'face_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'type': typing.Literal['solid3d_get_prev_adjacent_edge']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSolid3DGetPrevAdjacentEdge'>, 'config': {'title': 'OptionSolid3DGetPrevAdjacentEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSolid3DGetPrevAdjacentEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSolid3DGetPrevAdjacentEdge'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSolid3DGetPrevAdjacentEdge:94467867458848', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edge_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'face_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_prev_adjacent_edge', 'schema': {'expected': ['solid3d_get_prev_adjacent_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetPrevAdjacentEdge', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03aebd20, ), serializer: Fields( GeneralFieldsSerializer { fields: { "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b4b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_prev_adjacent_edge", }, expected_py: None, name: "literal['solid3d_get_prev_adjacent_edge']", }, ), }, ), ), required: true, }, "edge_id": SerField { key_py: Py( 0x00007fa14afd2a60, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "face_id": SerField { key_py: Py( 0x00007fa14b32c8a0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetPrevAdjacentEdge", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetPrevAdjacentEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edge_id", lookup_key: Simple { key: "edge_id", py_key: Py( 0x00007fa14ae75a70, ), path: LookupPath( [ S( "edge_id", Py( 0x00007fa14ae75a40, ), ), ], ), }, name_py: Py( 0x00007fa14afd2a60, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "face_id", lookup_key: Simple { key: "face_id", py_key: Py( 0x00007fa14ae75b00, ), path: LookupPath( [ S( "face_id", Py( 0x00007fa14ae75b30, ), ), ], ), }, name_py: Py( 0x00007fa14b32c8a0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14af1f770, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14af1f730, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae75b60, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae75b90, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b4b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_prev_adjacent_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b4b0, ), ], }, expected_repr: "'solid3d_get_prev_adjacent_edge'", name: "literal['solid3d_get_prev_adjacent_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_prev_adjacent_edge']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetPrevAdjacentEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03aebd20, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DGetPrevAdjacentEdge", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, edge_id: str, face_id: str, object_id: str, type: Literal['solid3d_get_prev_adjacent_edge'] = 'solid3d_get_prev_adjacent_edge') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'edge_id': FieldInfo(annotation=str, required=True), 'face_id': FieldInfo(annotation=str, required=True), 'object_id': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_prev_adjacent_edge'], required=False, default='solid3d_get_prev_adjacent_edge')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSolid3DShellFace(**data)[source][source]
Command for shelling a solid3d face
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'face_ids': typing.List[str], 'hollow': <class 'bool'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_id': <class 'str'>, 'shell_thickness': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': typing.Literal['solid3d_shell_face']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSolid3DShellFace'>, 'config': {'title': 'OptionSolid3DShellFace'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSolid3DShellFace'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSolid3DShellFace'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSolid3DShellFace:94467866561008', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'face_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'hollow': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'shell_thickness': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_shell_face', 'schema': {'expected': ['solid3d_shell_face'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DShellFace', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a109f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "shell_thickness": SerField { key_py: Py( 0x00007fa14afe06f0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "hollow": SerField { key_py: Py( 0x00007fa14afd2760, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f930d20, ), ), serializer: Bool( BoolSerializer, ), }, ), ), required: true, }, "face_ids": SerField { key_py: Py( 0x00007fa14afe06b0, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7cd70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_shell_face", }, expected_py: None, name: "literal['solid3d_shell_face']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "OptionSolid3DShellFace", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DShellFace", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "face_ids", lookup_key: Simple { key: "face_ids", py_key: Py( 0x00007fa14ae3ba30, ), path: LookupPath( [ S( "face_ids", Py( 0x00007fa14ae3ba70, ), ), ], ), }, name_py: Py( 0x00007fa14afe06b0, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "hollow", lookup_key: Simple { key: "hollow", py_key: Py( 0x00007fa14afe6af0, ), path: LookupPath( [ S( "hollow", Py( 0x00007fa14afe6b20, ), ), ], ), }, name_py: Py( 0x00007fa14afd2760, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f930d20, ), ), on_error: Raise, validator: Bool( BoolValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[bool]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14ae3b9b0, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14ae3b9f0, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "shell_thickness", lookup_key: Simple { key: "shell_thickness", py_key: Py( 0x00007fa14ae3b970, ), path: LookupPath( [ S( "shell_thickness", Py( 0x00007fa14ae3b930, ), ), ], ), }, name_py: Py( 0x00007fa14afe06f0, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ae3bb00, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe6b50, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe6b80, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7cd70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_shell_face": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7cd70, ), ], }, expected_repr: "'solid3d_shell_face'", name: "literal['solid3d_shell_face']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_shell_face']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DShellFace", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a109f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DShellFace", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, face_ids: List[str], hollow: bool = False, object_id: str, shell_thickness: kittycad.models.length_unit.LengthUnit, type: Literal['solid3d_shell_face'] = 'solid3d_shell_face') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'face_ids': FieldInfo(annotation=List[str], required=True), 'hollow': FieldInfo(annotation=bool, required=False, default=False), 'object_id': FieldInfo(annotation=str, required=True), 'shell_thickness': FieldInfo(annotation=LengthUnit, required=True), 'type': FieldInfo(annotation=Literal['solid3d_shell_face'], required=False, default='solid3d_shell_face')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
shell_thickness:
LengthUnit[source]
- class kittycad.models.modeling_cmd.OptionStartPath(**data)[source][source]
Start a new path.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['start_path']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionStartPath'>, 'config': {'title': 'OptionStartPath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionStartPath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionStartPath'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionStartPath:94467866433152', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'start_path', 'schema': {'expected': ['start_path'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionStartPath', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb039f1680, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7d030, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "start_path", }, expected_py: None, name: "literal['start_path']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionStartPath", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionStartPath", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14afe4e10, ), path: LookupPath( [ S( "type", Py( 0x00007fa14afe4ed0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7d030, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "start_path": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7d030, ), ], }, expected_repr: "'start_path'", name: "literal['start_path']", }, ), validate_default: false, copy_default: false, name: "default[literal['start_path']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionStartPath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb039f1680, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionStartPath", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['start_path'] = 'start_path') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['start_path'], required=False, default='start_path')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionSurfaceArea(**data)[source][source]
Get the surface area of entities in the scene or the default scene.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entity_ids': typing.List[str], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'output_unit': <enum 'UnitArea'>, 'type': typing.Literal['surface_area']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionSurfaceArea'>, 'config': {'title': 'OptionSurfaceArea'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionSurfaceArea'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionSurfaceArea'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionSurfaceArea:94467868085632', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitArea'>, 'members': [UnitArea.CM2, UnitArea.DM2, UnitArea.FT2, UnitArea.IN2, UnitArea.KM2, UnitArea.M2, UnitArea.MM2, UnitArea.YD2], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_area.UnitArea:94467863212352', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'surface_area', 'schema': {'expected': ['surface_area'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSurfaceArea', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b84d80, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007fa14b038970, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "output_unit": SerField { key_py: Py( 0x00007fa14bb4f470, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb036df140, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7d5f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "surface_area", }, expected_py: None, name: "literal['surface_area']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionSurfaceArea", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSurfaceArea", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007fa14adf4670, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007fa14adf4630, ), ), ], ), }, name_py: Py( 0x00007fa14b038970, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007fa14af38730, ), path: LookupPath( [ S( "output_unit", Py( 0x00007fa14adf46f0, ), ), ], ), }, name_py: Py( 0x00007fa14bb4f470, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb036df140, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "yd2": 7, "cm2": 0, "ft2": 2, "in2": 3, "dm2": 1, "mm2": 6, "m2": 5, "km2": 4, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c7950, ), Py( 0x00007fa14b3c79b0, ), Py( 0x00007fa14b3c7a10, ), Py( 0x00007fa14b3c7a70, ), Py( 0x00007fa14b3c7ad0, ), Py( 0x00007fa14b3c7b30, ), Py( 0x00007fa14b3c7b90, ), Py( 0x00007fa14b3c7bf0, ), ], }, missing: None, expected_repr: "'cm2', 'dm2', 'ft2', 'in2', 'km2', 'm2', 'mm2' or 'yd2'", strict: false, class_repr: "UnitArea", name: "str-enum[UnitArea]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc7780, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc77e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7d5f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "surface_area": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7d5f0, ), ], }, expected_repr: "'surface_area'", name: "literal['surface_area']", }, ), validate_default: false, copy_default: false, name: "default[literal['surface_area']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSurfaceArea", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b84d80, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSurfaceArea", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entity_ids: List[str], output_unit: kittycad.models.unit_area.UnitArea, type: Literal['surface_area'] = 'surface_area') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entity_ids': FieldInfo(annotation=List[str], required=True), 'output_unit': FieldInfo(annotation=UnitArea, required=True), 'type': FieldInfo(annotation=Literal['surface_area'], required=False, default='surface_area')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionTakeSnapshot(**data)[source][source]
Take a snapshot of the current view.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'format': <enum 'ImageFormat'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['take_snapshot']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionTakeSnapshot'>, 'config': {'title': 'OptionTakeSnapshot'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionTakeSnapshot'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionTakeSnapshot'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionTakeSnapshot:94467867800912', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'format': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'ImageFormat'>, 'members': [ImageFormat.PNG, ImageFormat.JPEG], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.image_format.ImageFormat:94467850098944', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'take_snapshot', 'schema': {'expected': ['take_snapshot'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionTakeSnapshot', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b3f550, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7d670, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "take_snapshot", }, expected_py: None, name: "literal['take_snapshot']", }, ), }, ), ), required: true, }, "format": SerField { key_py: Py( 0x00007fa14fa38b50, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb02a5d900, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionTakeSnapshot", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionTakeSnapshot", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "format", lookup_key: Simple { key: "format", py_key: Py( 0x00007fa14ad56b20, ), path: LookupPath( [ S( "format", Py( 0x00007fa14ad56b50, ), ), ], ), }, name_py: Py( 0x00007fa14fa38b50, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb02a5d900, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "jpeg": 1, "png": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b1185f0, ), Py( 0x00007fa14b118650, ), ], }, missing: None, expected_repr: "'png' or 'jpeg'", strict: false, class_repr: "ImageFormat", name: "str-enum[ImageFormat]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ad56670, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ad56700, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7d670, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "take_snapshot": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7d670, ), ], }, expected_repr: "'take_snapshot'", name: "literal['take_snapshot']", }, ), validate_default: false, copy_default: false, name: "default[literal['take_snapshot']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionTakeSnapshot", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b3f550, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionTakeSnapshot", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, format: kittycad.models.image_format.ImageFormat, type: Literal['take_snapshot'] = 'take_snapshot') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
-
format:
ImageFormat[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'format': FieldInfo(annotation=ImageFormat, required=True), 'type': FieldInfo(annotation=Literal['take_snapshot'], required=False, default='take_snapshot')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionUpdateAnnotation(**data)[source][source]
Update an annotation
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'annotation_id': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'options': <class 'kittycad.models.annotation_options.AnnotationOptions'>, 'type': typing.Literal['update_annotation']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <enum 'AnnotationLineEnd'>, 'members': [AnnotationLineEnd.NONE, AnnotationLineEnd.ARROW], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.annotation_line_end.AnnotationLineEnd:94467860848624', 'sub_type': 'str', 'type': 'enum'}], 'schema': {'cls': <class 'kittycad.models.modeling_cmd.OptionUpdateAnnotation'>, 'config': {'title': 'OptionUpdateAnnotation'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionUpdateAnnotation'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionUpdateAnnotation'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionUpdateAnnotation:94467867242048', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'annotation_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'options': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.annotation_options.AnnotationOptions'>, 'config': {'title': 'AnnotationOptions'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.annotation_options.AnnotationOptions'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.annotation_options.AnnotationOptions'>>]}, 'ref': 'kittycad.models.annotation_options.AnnotationOptions:94467859218624', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'color': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.color.Color'>, 'config': {'title': 'Color'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.color.Color'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.color.Color'>>]}, 'ref': 'kittycad.models.color.Color:94467859422064', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'a': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'b': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'g': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'r': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Color', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'line_ends': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.annotation_line_end_options.AnnotationLineEndOptions'>, 'config': {'title': 'AnnotationLineEndOptions'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.annotation_line_end_options.AnnotationLineEndOptions'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.annotation_line_end_options.AnnotationLineEndOptions'>>]}, 'ref': 'kittycad.models.annotation_line_end_options.AnnotationLineEndOptions:94467862299104', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'end': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.annotation_line_end.AnnotationLineEnd:94467860848624', 'type': 'definition-ref'}, 'type': 'model-field'}, 'start': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.annotation_line_end.AnnotationLineEnd:94467860848624', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'AnnotationLineEndOptions', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'line_width': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'position': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'text': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.annotation_text_options.AnnotationTextOptions'>, 'config': {'title': 'AnnotationTextOptions'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.annotation_text_options.AnnotationTextOptions'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.annotation_text_options.AnnotationTextOptions'>>]}, 'ref': 'kittycad.models.annotation_text_options.AnnotationTextOptions:94467859412128', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'point_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}, 'text': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'AnnotationTextAlignmentX'>, 'members': [AnnotationTextAlignmentX.LEFT, AnnotationTextAlignmentX.CENTER, AnnotationTextAlignmentX.RIGHT], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.annotation_text_alignment_x.AnnotationTextAlignmentX:94467862308832', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'AnnotationTextAlignmentY'>, 'members': [AnnotationTextAlignmentY.BOTTOM, AnnotationTextAlignmentY.CENTER, AnnotationTextAlignmentY.TOP], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.annotation_text_alignment_y.AnnotationTextAlignmentY:94467859410352', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'AnnotationTextOptions', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'AnnotationOptions', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'update_annotation', 'schema': {'expected': ['update_annotation'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionUpdateAnnotation', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ab6e40, ), serializer: Fields( GeneralFieldsSerializer { fields: { "annotation_id": SerField { key_py: Py( 0x00007fa14aff7c70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "options": SerField { key_py: Py( 0x00007fa14fa3aa38, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb033100c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "position": SerField { key_py: Py( 0x00007fa14e255bf0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055eb0330e750, ), serializer: Fields( GeneralFieldsSerializer { fields: { "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), }, ), }, ), ), required: true, }, "line_ends": SerField { key_py: Py( 0x00007fa14b1d3770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055eb036001e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "end": SerField { key_py: Py( 0x00007fa14fa38368, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: false, }, ), ), required: true, }, "start": SerField { key_py: Py( 0x00007fa14fa3bce8, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: false, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "AnnotationLineEndOptions", }, ), }, ), }, ), ), required: true, }, "text": SerField { key_py: Py( 0x00007fa14fa3c298, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055eb0333f4a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "point_size": SerField { key_py: Py( 0x00007fa14b2a67b0, ), alias: None, alias_py: None, serializer: Some( Int( IntSerializer, ), ), required: true, }, "text": SerField { key_py: Py( 0x00007fa14fa3c298, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb036027e0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb0333edb0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "AnnotationTextOptions", }, ), }, ), }, ), ), required: true, }, "line_width": SerField { key_py: Py( 0x00007fa14b1d37f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "color": SerField { key_py: Py( 0x00007fa14ea1a040, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055eb03341b70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "b": SerField { key_py: Py( 0x00007fa14fa3dd20, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "r": SerField { key_py: Py( 0x00007fa14fa3e020, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "a": SerField { key_py: Py( 0x00007fa14fa3dcf0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "g": SerField { key_py: Py( 0x00007fa14fa3de10, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Color", }, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "AnnotationOptions", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7eff0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "update_annotation", }, expected_py: None, name: "literal['update_annotation']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionUpdateAnnotation", }, ), definitions=[Enum(EnumSerializer { class: Py(0x55eb0349dff0), serializer: Some(Str(StrSerializer)) })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionUpdateAnnotation", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "annotation_id", lookup_key: Simple { key: "annotation_id", py_key: Py( 0x00007fa14af05730, ), path: LookupPath( [ S( "annotation_id", Py( 0x00007fa14af056f0, ), ), ], ), }, name_py: Py( 0x00007fa14aff7c70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "options", lookup_key: Simple { key: "options", py_key: Py( 0x00007fa14b0262b0, ), path: LookupPath( [ S( "options", Py( 0x00007fa14b0262e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3aa38, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "color", lookup_key: Simple { key: "color", py_key: Py( 0x00007fa14b0260d0, ), path: LookupPath( [ S( "color", Py( 0x00007fa14b026100, ), ), ], ), }, name_py: Py( 0x00007fa14ea1a040, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "a", lookup_key: Simple { key: "a", py_key: Py( 0x00007fa14fa3dcf0, ), path: LookupPath( [ S( "a", Py( 0x00007fa14fa3dcf0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3dcf0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "b", lookup_key: Simple { key: "b", py_key: Py( 0x00007fa14fa3dd20, ), path: LookupPath( [ S( "b", Py( 0x00007fa14fa3dd20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3dd20, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "g", lookup_key: Simple { key: "g", py_key: Py( 0x00007fa14fa3de10, ), path: LookupPath( [ S( "g", Py( 0x00007fa14fa3de10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3de10, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "r", lookup_key: Simple { key: "r", py_key: Py( 0x00007fa14fa3e020, ), path: LookupPath( [ S( "r", Py( 0x00007fa14fa3e020, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e020, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Color", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03341b70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Color", }, ), name: "nullable[Color]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Color]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "line_ends", lookup_key: Simple { key: "line_ends", py_key: Py( 0x00007fa14af057b0, ), path: LookupPath( [ S( "line_ends", Py( 0x00007fa14af05770, ), ), ], ), }, name_py: Py( 0x00007fa14b1d3770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "end", lookup_key: Simple { key: "end", py_key: Py( 0x00007fa14b026130, ), path: LookupPath( [ S( "end", Py( 0x00007fa14b026160, ), ), ], ), }, name_py: Py( 0x00007fa14fa38368, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "start", lookup_key: Simple { key: "start", py_key: Py( 0x00007fa14b026190, ), path: LookupPath( [ S( "start", Py( 0x00007fa14b0261c0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3bce8, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "AnnotationLineEndOptions", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb036001e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "AnnotationLineEndOptions", }, ), name: "nullable[AnnotationLineEndOptions]", }, ), validate_default: false, copy_default: false, name: "default[nullable[AnnotationLineEndOptions]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "line_width", lookup_key: Simple { key: "line_width", py_key: Py( 0x00007fa14af057f0, ), path: LookupPath( [ S( "line_width", Py( 0x00007fa14af05830, ), ), ], ), }, name_py: Py( 0x00007fa14b1d37f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "position", lookup_key: Simple { key: "position", py_key: Py( 0x00007fa14af05870, ), path: LookupPath( [ S( "position", Py( 0x00007fa14af058b0, ), ), ], ), }, name_py: Py( 0x00007fa14e255bf0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0330e750, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point3d", }, ), name: "nullable[Point3d]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Point3d]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "text", lookup_key: Simple { key: "text", py_key: Py( 0x00007fa14b026250, ), path: LookupPath( [ S( "text", Py( 0x00007fa14b026280, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c298, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "point_size", lookup_key: Simple { key: "point_size", py_key: Py( 0x00007fa14af058f0, ), path: LookupPath( [ S( "point_size", Py( 0x00007fa14af05930, ), ), ], ), }, name_py: Py( 0x00007fa14b2a67b0, ), validator: Int( IntValidator { strict: false, }, ), frozen: false, }, Field { name: "text", lookup_key: Simple { key: "text", py_key: Py( 0x00007fa14b0261f0, ), path: LookupPath( [ S( "text", Py( 0x00007fa14b026220, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c298, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb036027e0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "left": 0, "center": 1, "right": 2, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b361f10, ), Py( 0x00007fa14b361f70, ), Py( 0x00007fa14b361fd0, ), ], }, missing: None, expected_repr: "'left', 'center' or 'right'", strict: false, class_repr: "AnnotationTextAlignmentX", name: "str-enum[AnnotationTextAlignmentX]", }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb0333edb0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "top": 2, "bottom": 0, "center": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3620f0, ), Py( 0x00007fa14b362150, ), Py( 0x00007fa14b3621b0, ), ], }, missing: None, expected_repr: "'bottom', 'center' or 'top'", strict: false, class_repr: "AnnotationTextAlignmentY", name: "str-enum[AnnotationTextAlignmentY]", }, ), frozen: false, }, ], model_name: "AnnotationTextOptions", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0333f4a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "AnnotationTextOptions", }, ), name: "nullable[AnnotationTextOptions]", }, ), validate_default: false, copy_default: false, name: "default[nullable[AnnotationTextOptions]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "AnnotationOptions", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb033100c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "AnnotationOptions", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14b026310, ), path: LookupPath( [ S( "type", Py( 0x00007fa14b026340, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7eff0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "update_annotation": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7eff0, ), ], }, expected_repr: "'update_annotation'", name: "literal['update_annotation']", }, ), validate_default: false, copy_default: false, name: "default[literal['update_annotation']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionUpdateAnnotation", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ab6e40, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionUpdateAnnotation", }, ), definitions=[StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x55eb0349dff0), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"none": 0, "arrow": 1}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7fa14b361730), Py(0x7fa14b361c10)] }, missing: None, expected_repr: "'none' or 'arrow'", strict: false, class_repr: "AnnotationLineEnd", name: "str-enum[AnnotationLineEnd]" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, annotation_id: str, options: kittycad.models.annotation_options.AnnotationOptions, type: Literal['update_annotation'] = 'update_annotation') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'annotation_id': FieldInfo(annotation=str, required=True), 'options': FieldInfo(annotation=AnnotationOptions, required=True), 'type': FieldInfo(annotation=Literal['update_annotation'], required=False, default='update_annotation')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
-
options:
AnnotationOptions[source]
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionViewIsometric(**data)[source][source]
Fit the view to the scene with an isometric view.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'padding': <class 'float'>, 'type': typing.Literal['view_isometric']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionViewIsometric'>, 'config': {'title': 'OptionViewIsometric'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionViewIsometric'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionViewIsometric'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionViewIsometric:94467868238624', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'padding': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 0.0, 'schema': {'type': 'float'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'view_isometric', 'schema': {'expected': ['view_isometric'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionViewIsometric', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03baa320, ), serializer: Fields( GeneralFieldsSerializer { fields: { "padding": SerField { key_py: Py( 0x00007fa14ea196e0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14b1e7e70, ), ), serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd9f870, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "view_isometric", }, expected_py: None, name: "literal['view_isometric']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionViewIsometric", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionViewIsometric", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "padding", lookup_key: Simple { key: "padding", py_key: Py( 0x00007fa14ae00b10, ), path: LookupPath( [ S( "padding", Py( 0x00007fa14ae00c00, ), ), ], ), }, name_py: Py( 0x00007fa14ea196e0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14b1e7e70, ), ), on_error: Raise, validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), validate_default: false, copy_default: false, name: "default[float]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae00930, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae009f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd9f870, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "view_isometric": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd9f870, ), ], }, expected_repr: "'view_isometric'", name: "literal['view_isometric']", }, ), validate_default: false, copy_default: false, name: "default[literal['view_isometric']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionViewIsometric", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03baa320, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionViewIsometric", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, padding: float = 0.0, type: Literal['view_isometric'] = 'view_isometric') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'padding': FieldInfo(annotation=float, required=False, default=0.0), 'type': FieldInfo(annotation=Literal['view_isometric'], required=False, default='view_isometric')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionVolume(**data)[source][source]
Get the volume of entities in the scene or the default scene.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'entity_ids': typing.List[str], 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'output_unit': <enum 'UnitVolume'>, 'type': typing.Literal['volume']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionVolume'>, 'config': {'title': 'OptionVolume'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionVolume'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionVolume'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionVolume:94467868059776', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitVolume'>, 'members': [UnitVolume.CM3, UnitVolume.FT3, UnitVolume.IN3, UnitVolume.M3, UnitVolume.YD3, UnitVolume.USFLOZ, UnitVolume.USGAL, UnitVolume.L, UnitVolume.ML], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_volume.UnitVolume:94467863223008', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'volume', 'schema': {'expected': ['volume'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionVolume', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03b7e880, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14fa3c7f8, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "volume", }, expected_py: None, name: "literal['volume']", }, ), }, ), ), required: true, }, "entity_ids": SerField { key_py: Py( 0x00007fa14b038970, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "output_unit": SerField { key_py: Py( 0x00007fa14bb4f470, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb036e1ae0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "OptionVolume", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionVolume", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007fa14affcff0, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007fa14ade83f0, ), ), ], ), }, name_py: Py( 0x00007fa14b038970, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007fa14ad2dd30, ), path: LookupPath( [ S( "output_unit", Py( 0x00007fa14ade82b0, ), ), ], ), }, name_py: Py( 0x00007fa14bb4f470, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb036e1ae0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "m3": 3, "cm3": 0, "usgal": 6, "l": 7, "in3": 2, "ft3": 1, "yd3": 4, "usfloz": 5, "ml": 8, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c7f50, ), Py( 0x00007fa14b3c7fb0, ), Py( 0x00007fa14b38c050, ), Py( 0x00007fa14b38c0b0, ), Py( 0x00007fa14b38c110, ), Py( 0x00007fa14b38c170, ), Py( 0x00007fa14b38c1d0, ), Py( 0x00007fa14b38c230, ), Py( 0x00007fa14b38c290, ), ], }, missing: None, expected_repr: "'cm3', 'ft3', 'in3', 'm3', 'yd3', 'usfloz', 'usgal', 'l' or 'ml'", strict: false, class_repr: "UnitVolume", name: "str-enum[UnitVolume]", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14adc6b50, ), path: LookupPath( [ S( "type", Py( 0x00007fa14adc6bb0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14fa3c7f8, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "volume": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14fa3c7f8, ), ], }, expected_repr: "'volume'", name: "literal['volume']", }, ), validate_default: false, copy_default: false, name: "default[literal['volume']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionVolume", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03b7e880, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionVolume", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, entity_ids: List[str], output_unit: kittycad.models.unit_volume.UnitVolume, type: Literal['volume'] = 'volume') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'entity_ids': FieldInfo(annotation=List[str], required=True), 'output_unit': FieldInfo(annotation=UnitVolume, required=True), 'type': FieldInfo(annotation=Literal['volume'], required=False, default='volume')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
-
output_unit:
UnitVolume[source]
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.modeling_cmd.OptionZoomToFit(**data)[source][source]
Fit the view to the specified object(s).
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'animated': <class 'bool'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'object_ids': typing.List[str], 'padding': <class 'float'>, 'type': typing.Literal['zoom_to_fit']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.modeling_cmd.OptionZoomToFit'>, 'config': {'title': 'OptionZoomToFit'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.modeling_cmd.OptionZoomToFit'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionZoomToFit'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionZoomToFit:94467868228480', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'animated': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'object_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': [], 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'default'}, 'type': 'model-field'}, 'padding': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 0.0, 'schema': {'type': 'float'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'zoom_to_fit', 'schema': {'expected': ['zoom_to_fit'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionZoomToFit', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ba7b80, ), serializer: Fields( GeneralFieldsSerializer { fields: { "object_ids": SerField { key_py: Py( 0x00007fa14ae2c770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14add0600, ), ), serializer: List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd9fcb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "zoom_to_fit", }, expected_py: None, name: "literal['zoom_to_fit']", }, ), }, ), ), required: true, }, "animated": SerField { key_py: Py( 0x00007fa14b01ec30, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f930d20, ), ), serializer: Bool( BoolSerializer, ), }, ), ), required: true, }, "padding": SerField { key_py: Py( 0x00007fa14ea196e0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14b1e7e70, ), ), serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "OptionZoomToFit", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionZoomToFit", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "animated", lookup_key: Simple { key: "animated", py_key: Py( 0x00007fa14ae188b0, ), path: LookupPath( [ S( "animated", Py( 0x00007fa14ae18870, ), ), ], ), }, name_py: Py( 0x00007fa14b01ec30, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f930d20, ), ), on_error: Raise, validator: Bool( BoolValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[bool]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "object_ids", lookup_key: Simple { key: "object_ids", py_key: Py( 0x00007fa14ae18930, ), path: LookupPath( [ S( "object_ids", Py( 0x00007fa14ae188f0, ), ), ], ), }, name_py: Py( 0x00007fa14ae2c770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14add0600, ), ), on_error: Raise, validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( "list[str]", ), fail_fast: false, }, ), validate_default: false, copy_default: true, name: "default[list[str]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "padding", lookup_key: Simple { key: "padding", py_key: Py( 0x00007fa14ae01020, ), path: LookupPath( [ S( "padding", Py( 0x00007fa14ae01050, ), ), ], ), }, name_py: Py( 0x00007fa14ea196e0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14b1e7e70, ), ), on_error: Raise, validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), validate_default: false, copy_default: false, name: "default[float]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ae01080, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ae010b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd9fcb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "zoom_to_fit": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd9fcb0, ), ], }, expected_repr: "'zoom_to_fit'", name: "literal['zoom_to_fit']", }, ), validate_default: false, copy_default: false, name: "default[literal['zoom_to_fit']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionZoomToFit", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ba7b80, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionZoomToFit", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, animated: bool = False, object_ids: List[str] = [], padding: float = 0.0, type: Literal['zoom_to_fit'] = 'zoom_to_fit') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'animated': FieldInfo(annotation=bool, required=False, default=False), 'object_ids': FieldInfo(annotation=List[str], required=False, default=[]), 'padding': FieldInfo(annotation=float, required=False, default=0.0), 'type': FieldInfo(annotation=Literal['zoom_to_fit'], required=False, default='zoom_to_fit')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self